diff --git a/workspace/python/jupyter_notebook/spring_mass/3Mass_spring-nonlinear-fourier-BC_IC.ipynb b/workspace/python/jupyter_notebook/spring_mass/3Mass_spring-nonlinear-fourier-BC_IC.ipynb new file mode 100644 index 0000000..04dcffe --- /dev/null +++ b/workspace/python/jupyter_notebook/spring_mass/3Mass_spring-nonlinear-fourier-BC_IC.ipynb @@ -0,0 +1,1762 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "25324548-074a-484e-b8dd-ec6b54fdc9c8", + "metadata": {}, + "source": [ + "# **3-Mass System PINN**\n", + "\n", + "This repository demonstrates a Physics-Informed Neural Network (PINN) designed to solve an inverse problem for a 3-mass-spring system with a non-linear spring and **time-varying spring constants**. An inverse problem means we're trying to discover unknown parameters (here, spring constants and their switching time) by combining sparse observational data with the known physical laws of the system.\n", + "The PINN incorporates **hard constraints** by explicitly enforcing initial and boundary conditions during training:\n", + "\n", + "**Initial Conditions** at ( $t = 0$ ) are enforced exactly:\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "x_1(0) = x_{10}, \\quad x_2(0) = x_{20}, \\quad x_3(0) = x_{30}\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\dot{x}_1(0) = v_{10}, \\quad \\dot{x}_2(0) = v_{20}, \\quad \\dot{x}_3(0) = v_{30}\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "**Boundary / Intermediate / Final Conditions** (e.g., at time ( $t = T$ , $t = T/2$ )) may also be included as known constraints or observation points, and are enforced exactly within the network architecture or via augmented loss functions.\n", + "\n", + "\n", + "---\n", + "\n", + "## ODE System for Nonlinear 3-Mass-Spring with Trainable Parameters\n", + "\n", + "### System of Differential Equations\n", + "\n", + "The system models three masses connected via nonlinear springs. The dynamics of the system are governed by second-order ODEs:\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "m_1 \\frac{d^2 x_1}{dt^2} &= -k_1 x_1 - k_2 x_1^3 + k_2 x_2 \\\\\n", + "m_2 \\frac{d^2 x_2}{dt^2} &= k_2 x_1 - k_2 x_2 - k_3 x_2 + k_3 x_3 \\\\\n", + "m_3 \\frac{d^2 x_3}{dt^2} &= k_3 x_2 - k_3 x_3\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "Where:\n", + "\n", + "- displacements of masses ( $m_1, m_2, m_3$): $x_1, x_2, x_3$ \n", + "- spring constants: $k_1, k_2, k_3$ \n", + "- The spring between $m_1$ and $m_2$ is **nonlinear**, using a cubic term $x_1^3$\n", + "\n", + "\n", + "---\n", + "\n", + "### Dynamics\n", + "\n", + "- **Nonlinear spring force**: \n", + " The second spring includes a cubic term $x_1^3$, introducing nonlinearity.\n", + "\n", + "- **Step change (switching)** in the spring constants $k_1$ and $k_2$ \n", + " at a trainable switching time $t_{\\text{switch}}$.\n", + "\n", + "- **Heaviside-like function**: \n", + " Used to smoothly transition spring parameters around the switching point using a steep $\\tanh$ function.\n", + "\n", + "---\n", + "\n", + "### Spring Constant Switching\n", + "\n", + "A **switching mechanism** is implemented at a trainable switching time $t_{\\text{switch}}$ to model a change in material or system behavior:\n", + "\n", + "- **Before the switch**:\n", + " \n", + "$$\n", + " k_1 = k_{1a}, \\quad k_2 = k_{2a}\n", + "$$\n", + "\n", + "- **After the switch**:\n", + " \n", + "$$\n", + " k_1 = k_{1b}, \\quad k_2 = k_{2b}\n", + "$$\n", + "\n", + "- **Constant**:\n", + " \n", + " Parameter $k_3$ remains fixed for all time.\n", + "\n", + "---\n", + "\n", + "### Smooth Switching via Tanh\n", + "\n", + "To ensure differentiability for training with gradient-based methods, a smooth approximation of the step function is used:\n", + "\n", + "$$\n", + "\\text{weight\\_after}(t) = \\frac{1}{2} \\left( \\tanh\\left(c \\cdot (t - t_{\\text{switch}})\\right) + 1 \\right)\n", + "$$\n", + "\n", + "$$\n", + "\\text{weight\\_before}(t) = 1 - \\text{weight\\_after}(t)\n", + "$$\n", + "\n", + "Where $c$ is a steepness coefficient.\n", + "\n", + "The effective spring constants at time $t$ are computed as:\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "k_1(t) &= \\text{weight\\_before}(t) \\cdot k_{1a} + \\text{weight\\_after}(t) \\cdot k_{1b} \\\\\n", + "k_2(t) &= \\text{weight\\_before}(t) \\cdot k_{2a} + \\text{weight\\_after}(t) \\cdot k_{2b} \\\\\n", + "k_3(t) &= k_3 \\quad \\text{(constant)}\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "---\n", + "\n", + "### **Initial and Boundary Conditions**\n", + "\n", + "To ensure a well-posed problem and to guide the training of the PINN, initial and possibly intermediate/final conditions are applied:\n", + "\n", + "- **At initial time** $t = 0$:\n", + " - Initial displacements:\n", + " \n", + "$$\n", + " x_1(0) = x_{10}, \\quad x_2(0) = x_{20}, \\quad x_3(0) = x_{30}\n", + "$$\n", + "\n", + " - Initial velocities:\n", + "\n", + "$$ \n", + " \\dot{x}_1(0) = v_{10}, \\quad \\dot{x}_2(0) = v_{20}, \\quad \\dot{x}_3(0) = v_{30}\n", + "$$\n", + "\n", + "- **At intermediate time**: \n", + " Observational data or known values may be available at specific intermediate points $t_i$, which can be used to constrain the solution or train the PINN more effectively.\n", + "\n", + "- **At final time** $t = T$: \n", + " Displacement or velocity conditions at final time may be known or targeted in inverse problems:\n", + "\n", + " $x_j(T) = x_{jT}$ or $\\dot{x}_j(T) = v_{jT}$, for $j = 1, 2, 3$\n", + "\n", + "These conditions are integrated into the loss function of the PINN, helping guide the neural network to learn both the solution and the unknown physical parameters.\n" + ] + }, + { + "cell_type": "markdown", + "id": "d6b61426-570e-49d7-9639-a41307f80179", + "metadata": {}, + "source": [ + "## Understanding the Loss Functions in This PINN\n", + "This Physics-Informed Neural Network (PINN) is trained by minimizing a combination of three distinct loss functions. Each loss term ensures that the neural network's solution (`x_i(t)`) adheres to different aspects of the problem: observed data, governing physical laws, and specific known conditions (like initial states or intermediate boundary values).\n", + "\n", + "### Data Loss (`data_loss`)\n", + "- Purpose: This term measures how well the neural network's predicted solution matches the available observed data points.\n", + "- Calculation: It's typically calculated as the Mean Squared Error (MSE) between the PINN's output `x_pred` at the t_data points and the corresponding true `x_data` values.\n", + "- Role: It acts as a standard supervised learning loss, guiding the neural network to fit the discrete observations. Without data loss, the PINN might find a solution that satisfies the physics but doesn't align with any real-world measurements.\n", + "### Physics Loss (`physics_loss`)\n", + "- Purpose: This term enforces the governing physical laws (in this case, the Ordinary Differential Equations of the 3-mass system) throughout the entire time domain, not just at data points.\n", + "- Calculation: The core idea is to compute the \"residual\" of the differential equations. For each equation (e.g., for `m1 d2x1/dt2 - (-k1 x1 - k2 x1^3 + k2 x2) = 0`), the PINN predicts `x1, x2, x3` at a set of randomly sampled t_physics (collocation points). PyTorch's automatic differentiation (`torch.autograd.grad`) is then used to compute the necessary derivatives (first and second order, `dx/dt` and `d2x/dt2`) of the PINN's output with respect to time. These derivatives, along with the predicted positions, are plugged into the ODEs. The physics_loss is the Mean Squared Error of these residuals.\n", + "- Role: This is the \"physics-informed\" part. By driving the residuals to zero, the PINN learns a solution that inherently satisfies the differential equations. This helps the network generalize beyond sparse data points and provides robustness, especially when data is scarce or noisy. The non-linear spring term x1^3 is directly incorporated into these residual equations.\n", + "### Known Points Loss - **Initial/Boundary condition Loss** (`known_points_loss`)\n", + "\n", + "- Purpose: This loss term explicitly enforces the initial conditions (at t=0) and any other known \"**boundary**\" conditions (i.e., exact positions and velocities at specific **intermediate** ( at `t=2` or final time points `t=5`). These are typically \"**hard**\" constraints that the solution must satisfy.\n", + "- Calculation: Similar to data loss, but applied strictly at t_known_points. It calculates the MSE between the PINN's predicted positions (`x_pred_known`) and velocities (`dx_dt_known`) at these specific times and their known true values (`x_known_true`, `v_known_true`). The velocities are also computed using automatic differentiation.\n", + "- Role: This loss acts as a strong \"**anchor**\" for the PINN's solution. It ensures that the learned trajectory starts from the correct initial state and passes precisely through any other predefined states. This is crucial for obtaining a unique and physically realistic solution, as differential equations require such conditions for well-posedness.\n", + "\n", + "### **Adaptive Weighting**\n", + "\n", + "Each of these loss terms can have very different magnitudes and gradient scales. If not balanced, one loss might overpower the others, preventing the overall model from converging effectively. This code uses an adaptive weighting strategy based on the gradient norms of the individual loss components:\n", + "It calculates the gradient norm of the data_loss, physics_loss, and known_points_loss with respect to the network parameters.\n", + "It then adjusts the lambda (weighting factor) for the physics_loss and known_points_loss dynamically, often scaling them by the ratio of the data_loss gradient norm to their own gradient norm. This aims to equalize the \"pull\" that each loss exerts on the network's parameters, promoting more balanced and stable training.\n", + "The lambda_known_points in particular is often allowed to be quite high (e.g., up to 1000.0) because these conditions are considered highly critical." + ] + }, + { + "cell_type": "markdown", + "id": "c2c5bcbc-94f6-4a8a-bbbd-7faa835a5af5", + "metadata": {}, + "source": [ + "## **Break down the code section by section**\n", + "Now Let's in the following break down the code section by section:" + ] + }, + { + "cell_type": "markdown", + "id": "756dde4a-3d3e-4666-b6e7-dae6b06d60a5", + "metadata": {}, + "source": [ + "## **1. Initial Setup and Imports**\n", + "\n", + "```python\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import torch.nn.functional as F\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.integrate import solve_ivp\n", + "import math\n", + "\n", + "# Set device\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(f\"Using device: {device}\")\n", + "\n", + "```\n", + "\n", + "This section imports all necessary libraries:\n", + "\n", + "`torch`, `torch.nn`, `torch.optim`, `torch.nn.functional`: Core PyTorch modules for building and training neural networks.\n", + "`numpy`: For numerical operations, especially for generating synthetic data.\n", + "`matplotlib.pyplot`: For plotting and visualization.\n", + "`scipy.integrate.solve_ivp`: Used to numerically solve the Ordinary Differential Equations (ODEs) to generate our \"true\" synthetic data.\n", + "`math`: For mathematical constants like pi. It also sets the computation device to GPU if available (for faster training) and enables high-precision matrix multiplications for compatible NVIDIA GPUs, which can boost performance.\n" + ] + }, + { + "cell_type": "markdown", + "id": "da01b438-efa6-4b7a-a565-c7a6c30fe441", + "metadata": {}, + "source": [ + "## **2. Fourier Feature Encoding Helper Function**\n", + "\n", + "```python\n", + "def fourier_encode(x, freqs):\n", + " \"\"\"\n", + " Applies Fourier Feature Encoding to an input tensor.\n", + " ...\n", + " \"\"\"\n", + " # ... (function body) ...\n", + "```\n", + "\n", + "This function is a crucial feature engineering step. Neural Networks, especially simple Multi-Layer Perceptrons (MLPs), can sometimes struggle to learn high-frequency (rapidly changing) patterns or sharp transitions.\n", + "\n", + "What it does: It takes an input (x, which is time in our case) and transforms it into a much higher-dimensional space. For each original input feature, it generates sine and cosine waves at various frequencies (freqs).\n", + "Why it's useful for PINNs: The solutions to ODEs, especially non-linear ones or those with switching parameters, can be oscillatory or have sudden changes. Fourier features help the neural network \"see\" these high-frequency components more easily, allowing it to learn the underlying function more accurately and efficiently.\n" + ] + }, + { + "cell_type": "markdown", + "id": "8f7808dd-7803-488f-abea-0d4f4739fd1d", + "metadata": {}, + "source": [ + "## **3. The PINN Model (`ThreeMassPINN Class`)**\n", + "\n", + "```python\n", + "class ThreeMassPINN(nn.Module):\n", + " def __init__(self, num_layers=4, units_per_layer=256, output_dim=3,\n", + " fourier_features=True, num_fourier_modes=20):\n", + " super(ThreeMassPINN, self).__init__()\n", + "\n", + " # ... (initialization of layers and parameters) ...\n", + "\n", + " # Properties to get positive spring constants\n", + " @property\n", + " def k1a(self): return F.softplus(self.k1a_raw)\n", + " # ... (other k properties) ...\n", + "\n", + " # Property to get the constrained switching time\n", + " @property\n", + " def t_switch(self):\n", + " return self.min_t_switch + (self.max_t_switch - self.min_t_switch) * torch.sigmoid(self.t_switch_raw)\n", + "\n", + " def forward(self, t):\n", + " # ... (forward pass logic, including Fourier encoding) ...\n", + "\n", + " def compute_physics_loss(self, t_physics):\n", + " # ... (physics loss calculation) ...\n", + "\n", + " def compute_known_point_loss(self, t_known_points, x_known_true, v_known_true):\n", + " # ... (known points loss calculation) ...\n", + "```\n", + "\n", + "### Core of the PINN\n", + "\n", + "This is the core of our PINN. It inherits from `nn.Module`, making it a standard PyTorch neural network.\n", + "\n", + "---\n", + "\n", + "### `__init__` (Constructor)\n", + "\n", + "- **Defines the neural network architecture**:\n", + " - An input layer, multiple hidden layers, and an output layer.\n", + " - `units_per_layer` and `num_layers` control the network's complexity.\n", + "\n", + "- **Initializes parameters related to Fourier features**:\n", + " - `fourier_features` (boolean to enable/disable).\n", + " - `num_fourier_modes` (number of frequencies to use).\n", + " - The input layer's size adapts if Fourier features are enabled.\n", + "\n", + "- **Initializes trainable parameters (`nn.Parameter`)**:\n", + " - `k1a_raw`, `k1b_raw`, `k2a_raw`, `k2b_raw`, `k3_raw`: Raw spring constants.\n", + " - `t_switch_raw`: Raw parameter for the time at which `k1` and `k2` change.\n", + "\n", + "- **Initializes fixed parameters**:\n", + " - `m1`, `m2`, `m3` (masses).\n", + "\n", + "- **Properties (`@property`)**:\n", + " - Allow accessing `k1a`, `k1b`, etc., as attributes.\n", + " - Use `F.softplus(self.kX_raw)` to ensure positive spring constants.\n", + " - Use `torch.sigmoid(self.t_switch_raw)` to constrain `t_switch` to a range (e.g., between 1.0s and 4.0s).\n", + "\n", + "---\n", + "\n", + "### `forward(self, t)`\n", + "\n", + "- Defines how data flows through the network.\n", + "- Takes time `t` as input.\n", + "- If `fourier_features` is enabled:\n", + " - `t` is transformed by the `fourier_encode` function.\n", + "- The (possibly encoded) `t_processed` is passed through:\n", + " 1. `input_layer`\n", + " 2. `hidden_layers` with `F.mish` activation (a smooth alternative to ReLU)\n", + " 3. `output_layer`\n", + "- **Output**: Predicted positions of the three masses: `[x1(t), x2(t), x3(t)]`\n", + "\n", + "---\n", + "\n", + "### `compute_physics_loss(self, t_physics)`\n", + "\n", + "- Calculates the **physics loss**.\n", + "- `t_physics` is a batch of random collocation points.\n", + "- `t_physics.requires_grad_(True)` is set to enable autograd.\n", + "\n", + "#### Steps:\n", + "1. Predict `x1`, `x2`, `x3` at `t_physics`.\n", + "2. Use `torch.autograd.grad` to compute:\n", + " - First derivatives (velocities)\n", + " - Second derivatives (accelerations)\n", + "3. **Effective Spring Constants**:\n", + " - `k1_effective`, `k2_effective` are computed using `k1a`, `k1b`, `k2a`, `k2b`, and `t_switch`.\n", + " - A `tanh` function creates a smooth transition (step function approximation).\n", + "4. **ODEs (Residuals)**:\n", + " - Compare predicted accelerations to the right-hand side of the system's ODEs.\n", + " - Include non-linear terms like `x1**3`.\n", + " - Residuals: `r1`, `r2`, `r3`\n", + "5. **Loss**:\n", + " - `physics_loss` is the Mean Squared Error (MSE) of these residuals.\n", + " - Minimizing this trains the network to satisfy the ODEs.\n", + "\n", + "---\n", + "\n", + "### `compute_known_point_loss(self, t_known_points, x_known_true, v_known_true)`\n", + "\n", + "- Calculates the **known points loss** (initial/boundary conditions).\n", + "- Inputs:\n", + " - Specific `t_known_points` (e.g., 0.0, 2.0, 5.0)\n", + " - True positions `x_known_true` and velocities `v_known_true`\n", + "- Steps:\n", + " 1. Predict positions using the PINN.\n", + " 2. Use `torch.autograd.grad` to compute predicted velocities.\n", + " 3. Compute MSE between:\n", + " - Predicted and true positions\n", + " - Predicted and true velocities\n", + "\n", + "- This loss enforces that the solution starts at and passes through known physical states.\n" + ] + }, + { + "cell_type": "markdown", + "id": "8632026a-ae0f-438b-9eb4-4f31882f700d", + "metadata": {}, + "source": [ + "## **4. Synthetic Data Generation**\n", + "\n", + "```python\n", + "def odes_system_step_nonlinear(t, y, k1_val, k2_val, k3_val, m1, m2, m3):\n", + " # ... (ODE definition) ...\n", + "\n", + "# True parameters for data generation\n", + "true_k1a = 5.0\n", + "# ... (other true parameters) ...\n", + "\n", + "# ... (solve_ivp calls to generate data) ...\n", + "```\n", + "\n", + "\n", + "### `odes_system_step_nonlinear`\n", + "\n", + "- This standard Python function defines the exact, \"true\" behavior of our 3-mass system.\n", + "- It serves as the **ground truth** that the PINN aims to learn.\n", + "- Key features:\n", + " - Models a **step change** in `k1` and `k2` by segmenting the `solve_ivp` calls.\n", + " - Includes a **non-linearity**: `x1**3`.\n", + "\n", + "---\n", + "\n", + "### `solve_ivp`\n", + "\n", + "- `scipy.integrate.solve_ivp` is a robust numerical ODE solver.\n", + "- Used **twice**:\n", + " 1. For the time interval **before** the spring constant switch (`true_t_switch`).\n", + " 2. For the time interval **after**, with the corresponding `true_k` values.\n", + "- This generates:\n", + " - `t_data`: Synthetic time values.\n", + " - `x_data`: Corresponding positions.\n", + "\n", + "---\n", + "\n", + "### Known Points Extraction\n", + "\n", + "- Specific `known_times_np` are selected from the numerical solution.\n", + "- Their corresponding true values:\n", + " - `x_known_true_tensor` (positions)\n", + " - `v_known_true_tensor` (velocities)\n", + "- These are used in the **`known_points_loss`** function to enforce initial/boundary conditions.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "272ceb4a-d03f-4e0e-8ba3-8a3a887cbfa7", + "metadata": {}, + "source": [ + "## **5. Training Loop**\n", + "\n", + "```python\n", + "# PINN setup\n", + "pinn = ThreeMassPINN(...)\n", + "optimizer = optim.Adam(pinn.parameters(), lr=1e-3)\n", + "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=8000, eta_min=1e-6)\n", + "\n", + "# Training parameters\n", + "epochs = 20000\n", + "N_physics_points = 500\n", + "lambda_data = 1.0\n", + "lambda_physics = 0.05\n", + "lambda_known_points = 1.0\n", + "\n", + "# --- Adaptive Weighting Setup ---\n", + "history = { ... }\n", + "\n", + "# Training loop\n", + "for epoch in range(epochs):\n", + " optimizer.zero_grad()\n", + "\n", + " # 1. Data Loss\n", + " data_loss = ...\n", + "\n", + " # 2. Physics Loss\n", + " physics_loss = ...\n", + "\n", + " # 3. Known Points Loss\n", + " known_points_loss = ...\n", + "\n", + " # --- Adaptive Weighting Logic (Corrected Gradient-Norm Balancing) ---\n", + " # ... (gradient norm calculations and adaptive lambda adjustments) ...\n", + "\n", + " # Combined Loss using the adaptive weight\n", + " total_loss = lambda_data * data_loss + \\\n", + " adaptive_lambda_physics * physics_loss + \\\n", + " adaptive_lambda_known_points * known_points_loss\n", + "\n", + " total_loss.backward()\n", + " torch.nn.utils.clip_grad_norm_(pinn.parameters(), max_norm=1.0) # Gradient clipping\n", + " optimizer.step()\n", + " scheduler.step() # Update learning rate\n", + "\n", + " # ... (history logging and printing) ...\n", + "```\n", + "\n", + "\n", + "### Training the PINN\n", + "\n", + "This is where the magic happens – the **PINN learns!**\n", + "\n", + "---\n", + "\n", + "#### Model Instantiation\n", + "\n", + "- An instance of `ThreeMassPINN` is created with **Fourier features enabled**.\n", + "\n", + "---\n", + "\n", + "#### Optimizer\n", + "\n", + "- `optim.Adam` is used for its efficiency in training deep models.\n", + "- It updates:\n", + " - Neural network weights\n", + " - Trainable physical parameters (`k1a_raw`, `t_switch_raw`, etc.)\n", + "\n", + "---\n", + "\n", + "#### Learning Rate Scheduler\n", + "\n", + "- `CosineAnnealingLR` gradually decreases the learning rate over `T_max` epochs.\n", + "- Helps improve convergence during the later stages of training.\n", + "\n", + "---\n", + "\n", + "#### Training Parameters\n", + "\n", + "- **`epochs`**: Total number of training iterations.\n", + "- **`N_physics_points`**: Number of random points sampled per epoch to compute physics loss.\n", + "- **Loss weights**:\n", + " - `lambda_data`\n", + " - `lambda_physics`\n", + " - `lambda_known_points`\n", + "\n", + "---\n", + "\n", + "#### **Adaptive Weighting Logic**\n", + "\n", + "A key technique for PINNs:\n", + "\n", + "- Calculates **gradient norms** separately for:\n", + " - `data_loss`\n", + " - `physics_loss`\n", + " - `known_points_loss`\n", + "- Dynamically adjusts:\n", + " - `adaptive_lambda_physics`\n", + " - `adaptive_lambda_known_points`\n", + "- **Goal**: Scale the physics and known points losses so their gradients match the data loss gradient in magnitude.\n", + "- Prevents any one loss from dominating, ensuring **stable and balanced training**.\n", + "- `total_loss` is the sum of all three losses, each weighted by its (possibly adaptive) lambda.\n", + "\n", + "---\n", + "\n", + "#### Optimization Steps\n", + "\n", + "1. `optimizer.zero_grad()` \n", + " → Clears existing gradients.\n", + "\n", + "2. `total_loss.backward()` \n", + " → Computes gradients of `total_loss` with respect to all trainable parameters.\n", + "\n", + "3. `torch.nn.utils.clip_grad_norm_` \n", + " → Applies **gradient clipping** to avoid exploding gradients (common in deep, nonlinear models).\n", + "\n", + "4. `optimizer.step()` \n", + " → Updates network weights and physical parameters.\n", + "\n", + "5. `scheduler.step()` \n", + " → Updates the learning rate according to the scheduler.\n", + "\n", + "---\n", + "\n", + "#### History and Logging\n", + "\n", + "- Logs:\n", + " - Individual loss components\n", + " - Inferred physical parameters\n", + "- Provides periodic print statements to monitor **training progress** and **convergence**.\n" + ] + }, + { + "cell_type": "markdown", + "id": "420e38b6-2422-4056-b88e-be4f10ceea18", + "metadata": {}, + "source": [ + "## 6. **Visualization**\n", + "\n", + "```python\n", + "# ... (plotting loss history, inferred parameters, learning rates) ...\n", + "# Plot PINN predictions vs. true data\n", + "plt.figure(figsize=(12, 8))\n", + "# ... (plotting data points and PINN predictions) ...\n", + "# Plot the known points (initial/boundary conditions)\n", + "plt.plot(t_known_points.cpu().detach().numpy(), x_known_true_tensor.cpu().detach().numpy()[:, 0], 'rx', ...)\n", + "# ... (plotting inferred and true switch times) ...\n", + "plt.show()\n", + "```\n", + "\n", + "### Visualization Overview\n", + "\n", + "This section generates several plots to help visualize the **training process** and the **final results** of the PINN model:\n", + "\n", + "- **Loss History** \n", + " Displays how `total_loss`, `data_loss`, `physics_loss`, and `known_points_loss` evolve over training epochs. This helps in diagnosing convergence and optimization stability.\n", + "\n", + "- **Inferred Parameters** \n", + " Plots the learned values of `k1a`, `k1b`, `k2a`, `k2b`, `k3`, and `t_switch` across epochs, and compares them against their true values. This is critical for evaluating the accuracy of the inverse problem solution.\n", + "\n", + "- **Learning Rate and Adaptive Lambdas** \n", + " Shows the dynamic changes in:\n", + " - Learning rate (`lr`)\n", + " - Adaptive loss weights (`adaptive_lambda_physics`, `adaptive_lambda_known_points`) \n", + " These plots reveal how training balances different loss components over time.\n", + "\n", + "- **PINN Predictions vs. Synthetic Data** \n", + " The most important visualization:\n", + " - Plots the predicted trajectories (`x1`, `x2`, `x3`) from the trained PINN.\n", + " - Overlays the ground-truth synthetic data and known points (initial/boundary conditions).\n", + " - Includes both **inferred** and **true** `t_switch` values. \n", + " This enables a direct visual assessment of how accurately the PINN has learned the system’s dynamics.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "ac32c312-25cd-486e-b39d-4300bbc9bc1e", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b7a469c2-a22d-419d-b824-b1beb72ac718", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using device: cuda\n" + ] + } + ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import torch.nn.functional as F\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.integrate import solve_ivp\n", + "import math # Import math for pi\n", + "\n", + "# Set device\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(f\"Using device: {device}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b397c9bd-7109-48f2-a79a-78a337309e1c", + "metadata": {}, + "outputs": [], + "source": [ + "# --- Fourier Feature Encoding ---\n", + "def fourier_encode(x, freqs):\n", + " \"\"\"\n", + " Applies Fourier Feature Encoding to an input tensor.\n", + "\n", + " Args:\n", + " x (torch.Tensor): Input tensor (e.g., time `t`). Shape: (N, 1) or (N, D).\n", + " freqs (torch.Tensor): Frequencies for encoding. Shape: (num_modes,).\n", + "\n", + " Returns:\n", + " torch.Tensor: Concatenated original input, sine, and cosine features.\n", + " Shape: (N, D + D * num_modes * 2)\n", + " \"\"\"\n", + " # Reshape freqs for broadcasting: (1, 1, num_modes)\n", + " # x shape: (N, D)\n", + " # Resulting products shape: (N, D, num_modes)\n", + " x_expanded = x.unsqueeze(-1) # (N, D, 1)\n", + " freqs_expanded = freqs.unsqueeze(0).unsqueeze(0) # (1, 1, num_modes)\n", + "\n", + " # Compute products (N, D, num_modes)\n", + " products = x_expanded * freqs_expanded\n", + "\n", + " # Apply sine and cosine\n", + " sin_features = torch.sin(products) # (N, D, num_modes)\n", + " cos_features = torch.cos(products) # (N, D, num_modes)\n", + "\n", + " # Flatten the last two dimensions to get (N, D * num_modes)\n", + " sin_features = sin_features.view(x.shape[0], -1) # (N, D * num_modes)\n", + " cos_features = cos_features.view(x.shape[0], -1) # (N, D * num_modes)\n", + "\n", + " # Concatenate original input, sine, and cosine features\n", + " return torch.cat([x, sin_features, cos_features], dim=-1)\n", + "\n", + "\n", + "# --- 1. Define the 3-Mass System ODEs (conceptually, implemented in physics loss) ---\n", + "# Introducing a hypothetical non-linearity: k2 term becomes k2 * x1**3 for x1\n", + "# m1 d2x1/dt2 = -k1 x1 - k2 x1^3 + k2 x2\n", + "# m2 d2x2/dt2 = k2 x1 - k2 x2 - k3 x2 + k3 x3\n", + "# m3 d2x3/dt2 = k3 x2 - k3 x3" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a4aa36ca-faca-46ae-b77f-332d02ecbeb7", + "metadata": {}, + "outputs": [], + "source": [ + "# --- PINN that outputs xi(t) ---\n", + "class ThreeMassPINN(nn.Module):\n", + " def __init__(self, num_layers=4, units_per_layer=256, output_dim=3,\n", + " fourier_features=True, num_fourier_modes=20):\n", + " super(ThreeMassPINN, self).__init__()\n", + "\n", + " self.fourier_features = fourier_features\n", + " self.num_fourier_modes = num_fourier_modes\n", + "\n", + " input_dim_calc = 1 # Original input dimension (time)\n", + " if self.fourier_features:\n", + " # Each original input feature (1) is multiplied by 2 * num_fourier_modes (for sin/cos)\n", + " input_dim_calc += 1 * self.num_fourier_modes * 2\n", + " # Register frequencies for Fourier encoding\n", + " self.register_buffer(\n", + " \"freqs\", torch.exp(torch.linspace(0, math.pi, self.num_fourier_modes))\n", + " )\n", + "\n", + " self.input_layer = nn.Linear(input_dim_calc, units_per_layer)\n", + " self.hidden_layers = nn.ModuleList([\n", + " nn.Linear(units_per_layer, units_per_layer) for _ in range(num_layers - 1)\n", + " ])\n", + " self.output_layer = nn.Linear(units_per_layer, output_dim)\n", + "\n", + " # Trainable parameters for spring constants (before and after switch)\n", + " # Better initialization for k_raw parameters, assuming they are in a reasonable range (e.g., 1 to 20)\n", + " self.k1a_raw = nn.Parameter(torch.rand(1, device=device) * 5 + 5) # Initialized closer to expected values\n", + " self.k1b_raw = nn.Parameter(torch.rand(1, device=device) * 5 + 5)\n", + " self.k2a_raw = nn.Parameter(torch.rand(1, device=device) * 5 + 5)\n", + " self.k2b_raw = nn.Parameter(torch.rand(1, device=device) * 5 + 5)\n", + " self.k3_raw = nn.Parameter(torch.rand(1, device=device) * 5 + 5)\n", + "\n", + " # Trainable parameter for the switching time\n", + " self.min_t_switch = 1.0\n", + " self.max_t_switch = 4.0\n", + " self.t_switch_raw = nn.Parameter(torch.tensor([0.5], device=device))\n", + "\n", + " # Fixed mass parameters\n", + " self.m1 = torch.tensor(1.0, dtype=torch.float32, device=device)\n", + " self.m2 = torch.tensor(1.0, dtype=torch.float32, device=device)\n", + " self.m3 = torch.tensor(1.0, dtype=torch.float32, device=device)\n", + "\n", + " # Properties to get positive spring constants\n", + " @property\n", + " def k1a(self): return F.softplus(self.k1a_raw)\n", + " @property\n", + " def k1b(self): return F.softplus(self.k1b_raw)\n", + " @property\n", + " def k2a(self): return F.softplus(self.k2a_raw)\n", + " @property\n", + " def k2b(self): return F.softplus(self.k2b_raw)\n", + " @property\n", + " def k3(self): return F.softplus(self.k3_raw)\n", + "\n", + " # Property to get the constrained switching time\n", + " @property\n", + " def t_switch(self):\n", + " return self.min_t_switch + (self.max_t_switch - self.min_t_switch) * torch.sigmoid(self.t_switch_raw)\n", + "\n", + " def forward(self, t):\n", + " if t.dim() == 1:\n", + " t = t.unsqueeze(-1) # Ensure t is (N, 1)\n", + "\n", + " # Apply Fourier Feature Encoding if enabled\n", + " if self.fourier_features:\n", + " t_processed = fourier_encode(t, self.freqs)\n", + " else:\n", + " t_processed = t\n", + "\n", + " x = F.mish(self.input_layer(t_processed))\n", + " for layer in self.hidden_layers:\n", + " x = F.mish(layer(x))\n", + " return self.output_layer(x)\n", + "\n", + " # --- Computes residuals using autograd ---\n", + " def compute_physics_loss(self, t_physics):\n", + " t_physics.requires_grad_(True)\n", + "\n", + " x_pred = self(t_physics)\n", + " x1 = x_pred[:, 0:1]\n", + " x2 = x_pred[:, 1:2]\n", + " x3 = x_pred[:, 2:3]\n", + "\n", + " # Compute first derivatives (velocities)\n", + " dx1_dt = torch.autograd.grad(x1, t_physics, grad_outputs=torch.ones_like(x1), create_graph=True)[0]\n", + " dx2_dt = torch.autograd.grad(x2, t_physics, grad_outputs=torch.ones_like(x2), create_graph=True)[0]\n", + " dx3_dt = torch.autograd.grad(x3, t_physics, grad_outputs=torch.ones_like(x3), create_graph=True)[0]\n", + "\n", + " # Compute second derivatives (accelerations)\n", + " d2x1_dt2 = torch.autograd.grad(dx1_dt, t_physics, grad_outputs=torch.ones_like(dx1_dt), create_graph=True)[0]\n", + " d2x2_dt2 = torch.autograd.grad(dx2_dt, t_physics, grad_outputs=torch.ones_like(dx2_dt), create_graph=True)[0]\n", + " d2x3_dt2 = torch.autograd.grad(dx3_dt, t_physics, grad_outputs=torch.ones_like(dx3_dt), create_graph=True)[0]\n", + "\n", + " # Use a very steep tanh to approximate the heaviside function for spring constant switching\n", + " # Adjusted epsilon for better stability\n", + " epsilon = 1e-3\n", + " weight_before = 0.5 * (1.0 - torch.tanh((t_physics - self.t_switch) / epsilon))\n", + " weight_after = 0.5 * (1.0 + torch.tanh((t_physics - self.t_switch) / epsilon))\n", + "\n", + " k1_effective = self.k1a * weight_before + self.k1b * weight_after\n", + " k2_effective = self.k2a * weight_before + self.k2b * weight_after\n", + " k3_effective = self.k3\n", + "\n", + " # --- MODIFIED ODEs with non-linear parameter involvement ---\n", + " # m1 d2x1/dt2 = -k1 x1 - k2 x1^3 + k2 x2\n", + " # m2 d2x2/dt2 = k2 x1 - k2 x2 - k3 x2 + k3 x3\n", + " # m3 d2x3/dt2 = k3 x2 - k3 x3\n", + "\n", + " r1 = self.m1 * d2x1_dt2 - (-k1_effective * x1 - k2_effective * (x1**3) + k2_effective * x2) # NON-LINEAR TERM\n", + " r2 = self.m2 * d2x2_dt2 - (k2_effective * x1 - k2_effective * x2 - k3_effective * x2 + k3_effective * x3)\n", + " r3 = self.m3 * d2x3_dt2 - (k3_effective * x2 - k3_effective * x3)\n", + "\n", + " physics_loss = torch.mean(r1**2) + torch.mean(r2**2) + torch.mean(r3**2)\n", + " return physics_loss\n", + "\n", + " # --- Method to compute loss at known points (initial/boundary conditions) ---\n", + " def compute_known_point_loss(self, t_known_points, x_known_true, v_known_true):\n", + " t_known_points.requires_grad_(True) # Ensure gradients can be computed\n", + "\n", + " x_pred_known = self(t_known_points)\n", + " x1_pred_known = x_pred_known[:, 0:1]\n", + " x2_pred_known = x_pred_known[:, 1:2]\n", + " x3_pred_known = x_pred_known[:, 2:3]\n", + "\n", + " # Compute velocities at known points\n", + " dx1_dt_known = torch.autograd.grad(x1_pred_known, t_known_points, grad_outputs=torch.ones_like(x1_pred_known), create_graph=True)[0]\n", + " dx2_dt_known = torch.autograd.grad(x2_pred_known, t_known_points, grad_outputs=torch.ones_like(x2_pred_known), create_graph=True)[0]\n", + " dx3_dt_known = torch.autograd.grad(x3_pred_known, t_known_points, grad_outputs=torch.ones_like(x3_pred_known), create_graph=True)[0]\n", + " \n", + " # Loss for positions at known points\n", + " # Using [:, 0:1] to select the first column (x1), etc. for clarity and correctness\n", + " loss_x_known = (torch.mean((x1_pred_known - x_known_true[:, 0:1])**2) +\n", + " torch.mean((x2_pred_known - x_known_true[:, 1:2])**2) +\n", + " torch.mean((x3_pred_known - x_known_true[:, 2:3])**2))\n", + " \n", + " # Loss for velocities at known points\n", + " loss_v_known = (torch.mean((dx1_dt_known - v_known_true[:, 0:1])**2) +\n", + " torch.mean((dx2_dt_known - v_known_true[:, 1:2])**2) +\n", + " torch.mean((dx3_dt_known - v_known_true[:, 2:3])**2))\n", + "\n", + " return loss_x_known + loss_v_known\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "082b84cf-4d3e-4802-80d9-2aae1659603d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generated data shape: t_data=torch.Size([200, 1]), x_data=torch.Size([200, 3])\n" + ] + } + ], + "source": [ + "# --- Fit the PINN using both data and physics loss ---\n", + "\n", + "# Generate some synthetic data with a step change AND non-linear k2 effect\n", + "def odes_system_step_nonlinear(t, y, k1_val, k2_val, k3_val, m1, m2, m3):\n", + " x1, v1, x2, v2, x3, v3 = y\n", + " # Modified ODE for d2x1_dt2 to match the non-linearity\n", + " d2x1_dt2 = (1/m1) * (-k1_val * x1 - k2_val * (x1**3) + k2_val * x2)\n", + " d2x2_dt2 = (1/m2) * (k2_val * x1 - k2_val * x2 - k3_val * x2 + k3_val * x3)\n", + " d2x3_dt2 = (1/m3) * (k3_val * x2 - k3_val * x3)\n", + " return [v1, d2x1_dt2, v2, d2x2_dt2, v3, d2x3_dt2]\n", + "\n", + "# True parameters for data generation\n", + "true_k1a = 5.0\n", + "true_k1b = 15.0\n", + "true_k2a = 10.0 # This k2 will be multiplied by x1^3\n", + "true_k2b = 2.0\n", + "true_k3 = 7.0\n", + "true_t_switch = 2.5\n", + "\n", + "masses = [1.0, 1.0, 1.0]\n", + "\n", + "# Initial conditions (True values for data generation)\n", + "x1_0, v1_0 = 0.1, 0.0\n", + "x2_0, v2_0 = 0.0, 0.0\n", + "x3_0, v3_0 = 0.0, 0.0\n", + "initial_conditions = [x1_0, v1_0, x2_0, v2_0, x3_0, v3_0]\n", + "\n", + "time_span = (0, 5) # seconds\n", + "t_data_np = np.linspace(time_span[0], time_span[1], 200) # More data points for complex dynamics\n", + "\n", + "# Solve the ODEs in two segments for synthetic data\n", + "sol1 = solve_ivp(\n", + " odes_system_step_nonlinear, # Use the new nonlinear ODE function\n", + " (time_span[0], true_t_switch),\n", + " initial_conditions,\n", + " args=(true_k1a, true_k2a, true_k3, masses[0], masses[1], masses[2]),\n", + " t_eval=t_data_np[t_data_np < true_t_switch].flatten(),\n", + " method='RK45',\n", + " rtol=1e-9, atol=1e-9, # Higher precision for solver\n", + " dense_output=True # ADDED: Ensure dense output is enabled\n", + ")\n", + "\n", + "final_y_sol1 = sol1.y[:, -1]\n", + "\n", + "sol2 = solve_ivp(\n", + " odes_system_step_nonlinear, # Use the new nonlinear ODE function\n", + " (true_t_switch, time_span[1]),\n", + " final_y_sol1,\n", + " args=(true_k1b, true_k2b, true_k3, masses[0], masses[1], masses[2]),\n", + " t_eval=t_data_np[t_data_np >= true_t_switch].flatten(),\n", + " method='RK45',\n", + " rtol=1e-9, atol=1e-9, # Higher precision for solver\n", + " dense_output=True # ADDED: Ensure dense output is enabled\n", + ")\n", + "\n", + "# Combine the results\n", + "t_data_combined = np.concatenate((sol1.t, sol2.t))\n", + "y_data_combined = np.concatenate((sol1.y.T, sol2.y.T)) # y contains [x1, v1, x2, v2, x3, v3]\n", + "\n", + "# Sort the data by time\n", + "sort_indices = np.argsort(t_data_combined)\n", + "t_data_combined = t_data_combined[sort_indices]\n", + "y_data_combined = y_data_combined[sort_indices]\n", + "\n", + "# Extract x_data and v_data from combined y_data\n", + "x_data_np = y_data_combined[:, [0, 2, 4]] # Positions\n", + "v_data_np = y_data_combined[:, [1, 3, 5]] # Velocities\n", + "\n", + "\n", + "# Convert generated data to PyTorch tensors and move to device\n", + "t_data = torch.tensor(t_data_combined, dtype=torch.float32, device=device).unsqueeze(-1)\n", + "x_data = torch.tensor(x_data_np, dtype=torch.float32, device=device)\n", + "\n", + "print(f\"Generated data shape: t_data={t_data.shape}, x_data={x_data.shape}\")\n", + "\n", + "# --- Define Known Points (Initial/Boundary Conditions) ---\n", + "# We will enforce conditions at t=0, t=2.0, and t=5.0\n", + "# t=0 is the initial condition\n", + "# t=2.0 is an intermediate \"boundary\" condition\n", + "# t=5.0 is a final \"boundary\" condition\n", + "\n", + "known_times_np = np.array([0.0, 2.0, 5.0])\n", + "# Interpolate true positions and velocities at these known times from scipy solution\n", + "# Ensure sol1.sol and sol2.sol are callable by dense_output=True in solve_ivp\n", + "known_states_at_times = []\n", + "for t_val in known_times_np:\n", + " # Use sol1.sol for times before or at the switch, sol2.sol for times after\n", + " # Note: for t_val == true_t_switch, both sol1.sol and sol2.sol would work\n", + " # if the solution is continuous across the switch point.\n", + " if t_val <= true_t_switch: # Use <= to ensure switch point is covered by sol1 if it's the exact end of sol1's range\n", + " known_states_at_times.append(sol1.sol(t_val))\n", + " else:\n", + " known_states_at_times.append(sol2.sol(t_val))\n", + "known_states_at_times = np.array(known_states_at_times).T # Transpose to get (6, num_known_points)\n", + "\n", + "t_known_points = torch.tensor(known_times_np, dtype=torch.float32, device=device).unsqueeze(-1)\n", + "x_known_true_tensor = torch.tensor(known_states_at_times[[0,2,4], :].T, dtype=torch.float32, device=device) # Positions\n", + "v_known_true_tensor = torch.tensor(known_states_at_times[[1,3,5], :].T, dtype=torch.float32, device=device) # Velocities\n", + "\n", + "\n", + "# PINN setup\n", + "pinn = ThreeMassPINN(units_per_layer=256, fourier_features=True, num_fourier_modes=20).to(device)\n", + "optimizer = optim.Adam(pinn.parameters(), lr=1e-3)\n", + "\n", + "# Learning Rate Scheduler (Cosine Annealing)\n", + "# Corrected: lr_scheduler is a direct attribute of optim, not optim.optim\n", + "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=8000, eta_min=1e-6)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "dd88a497-0152-4b8f-9283-ff77fd026490", + "metadata": {}, + "outputs": [], + "source": [ + "# Training parameters\n", + "epochs = 20000\n", + "N_physics_points = 500 # Increased physics points for better resolution\n", + "lambda_data = 1.0\n", + "lambda_physics = 0.05 # Initial weight, will be adaptively adjusted\n", + "lambda_known_points = 1.0 # Initial weight for known conditions (will be adaptively adjusted)\n", + "\n", + "# --- Adaptive Weighting Setup ---\n", + "history = {\n", + " 'loss': [], 'data_loss': [], 'physics_loss': [], 'scaled_physics_loss': [],\n", + " 'known_points_loss': [], 'scaled_known_points_loss': [], # Added scaled_known_points_loss\n", + " 'k1a': [], 'k1b': [], 'k2a': [], 'k2b': [], 'k3': [], 't_switch': [],\n", + " 'current_lr': [], 'adaptive_lambda_physics': [], 'adaptive_lambda_known_points': [] # Added adaptive_lambda_known_points\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "42e80ffc-7cbc-493d-b224-c14dd58a1c88", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0/20000, LR: 1.00e-03, Loss: 0.495056, Data Loss: 0.296565, Physics Loss (Raw): 6.520285, Scaled Physics Loss: 0.006520, Known Points Loss (Raw): 1.766981, Scaled Known Points Loss: 0.191971\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.1086\n", + " k1a: 8.3753 (True: 5.0), k1b: 6.1539 (True: 15.0)\n", + " k2a: 9.8354 (True: 10.0), k2b: 6.4031 (True: 2.0)\n", + " k3: 8.1518 (True: 7.0), t_switch: 2.8667 (True: 2.5)\n", + "Epoch 200/20000, LR: 9.98e-04, Loss: 0.012850, Data Loss: 0.003482, Physics Loss (Raw): 8.948701, Scaled Physics Loss: 0.008949, Known Points Loss (Raw): 0.002097, Scaled Known Points Loss: 0.000419\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.1998\n", + " k1a: 8.4117 (True: 5.0), k1b: 6.3760 (True: 15.0)\n", + " k2a: 9.7634 (True: 10.0), k2b: 6.2123 (True: 2.0)\n", + " k3: 8.3019 (True: 7.0), t_switch: 2.7922 (True: 2.5)\n", + "Epoch 400/20000, LR: 9.94e-04, Loss: 0.011890, Data Loss: 0.002614, Physics Loss (Raw): 8.835401, Scaled Physics Loss: 0.008835, Known Points Loss (Raw): 0.002881, Scaled Known Points Loss: 0.000441\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.1529\n", + " k1a: 8.3891 (True: 5.0), k1b: 6.5820 (True: 15.0)\n", + " k2a: 9.7384 (True: 10.0), k2b: 6.0132 (True: 2.0)\n", + " k3: 8.4116 (True: 7.0), t_switch: 2.7437 (True: 2.5)\n", + "Epoch 600/20000, LR: 9.86e-04, Loss: 0.012432, Data Loss: 0.002176, Physics Loss (Raw): 9.455631, Scaled Physics Loss: 0.009456, Known Points Loss (Raw): 0.011386, Scaled Known Points Loss: 0.000801\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0703\n", + " k1a: 8.3152 (True: 5.0), k1b: 6.7844 (True: 15.0)\n", + " k2a: 9.7604 (True: 10.0), k2b: 5.8163 (True: 2.0)\n", + " k3: 8.5128 (True: 7.0), t_switch: 2.6951 (True: 2.5)\n", + "Epoch 800/20000, LR: 9.75e-04, Loss: 0.011142, Data Loss: 0.003073, Physics Loss (Raw): 7.235332, Scaled Physics Loss: 0.007235, Known Points Loss (Raw): 0.005180, Scaled Known Points Loss: 0.000834\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.1610\n", + " k1a: 8.1767 (True: 5.0), k1b: 6.9848 (True: 15.0)\n", + " k2a: 9.8399 (True: 10.0), k2b: 5.6236 (True: 2.0)\n", + " k3: 8.5935 (True: 7.0), t_switch: 2.6422 (True: 2.5)\n", + "Epoch 1000/20000, LR: 9.62e-04, Loss: 0.010233, Data Loss: 0.002280, Physics Loss (Raw): 7.676485, Scaled Physics Loss: 0.007676, Known Points Loss (Raw): 0.000555, Scaled Known Points Loss: 0.000276\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.4973\n", + " k1a: 7.9959 (True: 5.0), k1b: 7.1819 (True: 15.0)\n", + " k2a: 9.9601 (True: 10.0), k2b: 5.4348 (True: 2.0)\n", + " k3: 8.6479 (True: 7.0), t_switch: 2.6002 (True: 2.5)\n", + "Epoch 1200/20000, LR: 9.45e-04, Loss: 0.009196, Data Loss: 0.002145, Physics Loss (Raw): 6.752384, Scaled Physics Loss: 0.006752, Known Points Loss (Raw): 0.000497, Scaled Known Points Loss: 0.000299\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.6006\n", + " k1a: 7.8013 (True: 5.0), k1b: 7.3768 (True: 15.0)\n", + " k2a: 10.1009 (True: 10.0), k2b: 5.2532 (True: 2.0)\n", + " k3: 8.6853 (True: 7.0), t_switch: 2.5585 (True: 2.5)\n", + "Epoch 1400/20000, LR: 9.26e-04, Loss: 0.008426, Data Loss: 0.001365, Physics Loss (Raw): 6.771526, Scaled Physics Loss: 0.006772, Known Points Loss (Raw): 0.001459, Scaled Known Points Loss: 0.000289\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.1984\n", + " k1a: 7.5976 (True: 5.0), k1b: 7.5672 (True: 15.0)\n", + " k2a: 10.2492 (True: 10.0), k2b: 5.0780 (True: 2.0)\n", + " k3: 8.6881 (True: 7.0), t_switch: 2.5146 (True: 2.5)\n", + "Epoch 1600/20000, LR: 9.04e-04, Loss: 0.007711, Data Loss: 0.001635, Physics Loss (Raw): 5.718035, Scaled Physics Loss: 0.005718, Known Points Loss (Raw): 0.000472, Scaled Known Points Loss: 0.000358\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.7587\n", + " k1a: 7.3923 (True: 5.0), k1b: 7.7519 (True: 15.0)\n", + " k2a: 10.3970 (True: 10.0), k2b: 4.9100 (True: 2.0)\n", + " k3: 8.6922 (True: 7.0), t_switch: 2.4776 (True: 2.5)\n", + "Epoch 1800/20000, LR: 8.80e-04, Loss: 0.006975, Data Loss: 0.001317, Physics Loss (Raw): 5.456973, Scaled Physics Loss: 0.005457, Known Points Loss (Raw): 0.000395, Scaled Known Points Loss: 0.000201\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.5102\n", + " k1a: 7.1937 (True: 5.0), k1b: 7.9299 (True: 15.0)\n", + " k2a: 10.5390 (True: 10.0), k2b: 4.7504 (True: 2.0)\n", + " k3: 8.6658 (True: 7.0), t_switch: 2.4344 (True: 2.5)\n", + "Epoch 2000/20000, LR: 8.54e-04, Loss: 0.007418, Data Loss: 0.001675, Physics Loss (Raw): 5.414180, Scaled Physics Loss: 0.005414, Known Points Loss (Raw): 0.000724, Scaled Known Points Loss: 0.000329\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.4543\n", + " k1a: 7.0108 (True: 5.0), k1b: 8.1006 (True: 15.0)\n", + " k2a: 10.6716 (True: 10.0), k2b: 4.5989 (True: 2.0)\n", + " k3: 8.6284 (True: 7.0), t_switch: 2.4055 (True: 2.5)\n", + "Epoch 2200/20000, LR: 8.25e-04, Loss: 0.006737, Data Loss: 0.001052, Physics Loss (Raw): 5.498096, Scaled Physics Loss: 0.005498, Known Points Loss (Raw): 0.000474, Scaled Known Points Loss: 0.000187\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.3938\n", + " k1a: 6.8419 (True: 5.0), k1b: 8.2618 (True: 15.0)\n", + " k2a: 10.7973 (True: 10.0), k2b: 4.4553 (True: 2.0)\n", + " k3: 8.5737 (True: 7.0), t_switch: 2.3814 (True: 2.5)\n", + "Epoch 2400/20000, LR: 7.94e-04, Loss: 0.005301, Data Loss: 0.000998, Physics Loss (Raw): 4.146111, Scaled Physics Loss: 0.004146, Known Points Loss (Raw): 0.000424, Scaled Known Points Loss: 0.000157\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.3709\n", + " k1a: 6.6850 (True: 5.0), k1b: 8.4145 (True: 15.0)\n", + " k2a: 10.9154 (True: 10.0), k2b: 4.3201 (True: 2.0)\n", + " k3: 8.5064 (True: 7.0), t_switch: 2.3640 (True: 2.5)\n", + "Epoch 2600/20000, LR: 7.61e-04, Loss: 0.005299, Data Loss: 0.000972, Physics Loss (Raw): 4.203487, Scaled Physics Loss: 0.004203, Known Points Loss (Raw): 0.000225, Scaled Known Points Loss: 0.000124\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.5498\n", + " k1a: 6.5452 (True: 5.0), k1b: 8.5591 (True: 15.0)\n", + " k2a: 11.0208 (True: 10.0), k2b: 4.1925 (True: 2.0)\n", + " k3: 8.4367 (True: 7.0), t_switch: 2.3590 (True: 2.5)\n", + "Epoch 2800/20000, LR: 7.27e-04, Loss: 0.005609, Data Loss: 0.000909, Physics Loss (Raw): 4.566390, Scaled Physics Loss: 0.004566, Known Points Loss (Raw): 0.000274, Scaled Known Points Loss: 0.000133\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.4872\n", + " k1a: 6.4198 (True: 5.0), k1b: 8.6966 (True: 15.0)\n", + " k2a: 11.1148 (True: 10.0), k2b: 4.0729 (True: 2.0)\n", + " k3: 8.3734 (True: 7.0), t_switch: 2.3494 (True: 2.5)\n", + "Epoch 3000/20000, LR: 6.91e-04, Loss: 0.004997, Data Loss: 0.000827, Physics Loss (Raw): 3.982330, Scaled Physics Loss: 0.003982, Known Points Loss (Raw): 0.000947, Scaled Known Points Loss: 0.000187\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.1977\n", + " k1a: 6.3052 (True: 5.0), k1b: 8.8245 (True: 15.0)\n", + " k2a: 11.1993 (True: 10.0), k2b: 3.9604 (True: 2.0)\n", + " k3: 8.3091 (True: 7.0), t_switch: 2.3515 (True: 2.5)\n", + "Epoch 3200/20000, LR: 6.55e-04, Loss: 0.004562, Data Loss: 0.000815, Physics Loss (Raw): 3.601402, Scaled Physics Loss: 0.003601, Known Points Loss (Raw): 0.000485, Scaled Known Points Loss: 0.000146\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.3012\n", + " k1a: 6.2024 (True: 5.0), k1b: 8.9460 (True: 15.0)\n", + " k2a: 11.2723 (True: 10.0), k2b: 3.8556 (True: 2.0)\n", + " k3: 8.2456 (True: 7.0), t_switch: 2.3525 (True: 2.5)\n", + "Epoch 3400/20000, LR: 6.17e-04, Loss: 0.004572, Data Loss: 0.000549, Physics Loss (Raw): 3.917789, Scaled Physics Loss: 0.003918, Known Points Loss (Raw): 0.000237, Scaled Known Points Loss: 0.000106\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.4467\n", + " k1a: 6.1089 (True: 5.0), k1b: 9.0605 (True: 15.0)\n", + " k2a: 11.3358 (True: 10.0), k2b: 3.7576 (True: 2.0)\n", + " k3: 8.1845 (True: 7.0), t_switch: 2.3538 (True: 2.5)\n", + "Epoch 3600/20000, LR: 5.78e-04, Loss: 0.003879, Data Loss: 0.000709, Physics Loss (Raw): 3.070932, Scaled Physics Loss: 0.003071, Known Points Loss (Raw): 0.000179, Scaled Known Points Loss: 0.000099\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.5525\n", + " k1a: 6.0264 (True: 5.0), k1b: 9.1675 (True: 15.0)\n", + " k2a: 11.3885 (True: 10.0), k2b: 3.6668 (True: 2.0)\n", + " k3: 8.1275 (True: 7.0), t_switch: 2.3601 (True: 2.5)\n", + "Epoch 3800/20000, LR: 5.39e-04, Loss: 0.003731, Data Loss: 0.000657, Physics Loss (Raw): 2.931577, Scaled Physics Loss: 0.002932, Known Points Loss (Raw): 0.000461, Scaled Known Points Loss: 0.000142\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.3093\n", + " k1a: 5.9517 (True: 5.0), k1b: 9.2682 (True: 15.0)\n", + " k2a: 11.4324 (True: 10.0), k2b: 3.5831 (True: 2.0)\n", + " k3: 8.0775 (True: 7.0), t_switch: 2.3605 (True: 2.5)\n", + "Epoch 4000/20000, LR: 5.00e-04, Loss: 0.003933, Data Loss: 0.000618, Physics Loss (Raw): 3.213946, Scaled Physics Loss: 0.003214, Known Points Loss (Raw): 0.000323, Scaled Known Points Loss: 0.000101\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.3138\n", + " k1a: 5.8850 (True: 5.0), k1b: 9.3613 (True: 15.0)\n", + " k2a: 11.4687 (True: 10.0), k2b: 3.5062 (True: 2.0)\n", + " k3: 8.0246 (True: 7.0), t_switch: 2.3631 (True: 2.5)\n", + "Epoch 4200/20000, LR: 4.61e-04, Loss: 0.003278, Data Loss: 0.000636, Physics Loss (Raw): 2.560062, Scaled Physics Loss: 0.002560, Known Points Loss (Raw): 0.000167, Scaled Known Points Loss: 0.000082\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.4913\n", + " k1a: 5.8268 (True: 5.0), k1b: 9.4479 (True: 15.0)\n", + " k2a: 11.4960 (True: 10.0), k2b: 3.4356 (True: 2.0)\n", + " k3: 7.9780 (True: 7.0), t_switch: 2.3600 (True: 2.5)\n", + "Epoch 4400/20000, LR: 4.22e-04, Loss: 0.003512, Data Loss: 0.000658, Physics Loss (Raw): 2.770236, Scaled Physics Loss: 0.002770, Known Points Loss (Raw): 0.000082, Scaled Known Points Loss: 0.000083\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 1.0114\n", + " k1a: 5.7715 (True: 5.0), k1b: 9.5279 (True: 15.0)\n", + " k2a: 11.5198 (True: 10.0), k2b: 3.3712 (True: 2.0)\n", + " k3: 7.9379 (True: 7.0), t_switch: 2.3565 (True: 2.5)\n", + "Epoch 4600/20000, LR: 3.84e-04, Loss: 0.003104, Data Loss: 0.000561, Physics Loss (Raw): 2.484444, Scaled Physics Loss: 0.002484, Known Points Loss (Raw): 0.000107, Scaled Known Points Loss: 0.000058\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.5423\n", + " k1a: 5.7218 (True: 5.0), k1b: 9.6010 (True: 15.0)\n", + " k2a: 11.5387 (True: 10.0), k2b: 3.3125 (True: 2.0)\n", + " k3: 7.8973 (True: 7.0), t_switch: 2.3605 (True: 2.5)\n", + "Epoch 4800/20000, LR: 3.46e-04, Loss: 0.003285, Data Loss: 0.000469, Physics Loss (Raw): 2.768544, Scaled Physics Loss: 0.002769, Known Points Loss (Raw): 0.000057, Scaled Known Points Loss: 0.000048\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.8470\n", + " k1a: 5.6773 (True: 5.0), k1b: 9.6672 (True: 15.0)\n", + " k2a: 11.5518 (True: 10.0), k2b: 3.2597 (True: 2.0)\n", + " k3: 7.8618 (True: 7.0), t_switch: 2.3597 (True: 2.5)\n", + "Epoch 5000/20000, LR: 3.09e-04, Loss: 0.002807, Data Loss: 0.000430, Physics Loss (Raw): 2.330366, Scaled Physics Loss: 0.002330, Known Points Loss (Raw): 0.000070, Scaled Known Points Loss: 0.000046\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.6563\n", + " k1a: 5.6365 (True: 5.0), k1b: 9.7271 (True: 15.0)\n", + " k2a: 11.5624 (True: 10.0), k2b: 3.2122 (True: 2.0)\n", + " k3: 7.8304 (True: 7.0), t_switch: 2.3576 (True: 2.5)\n", + "Epoch 5200/20000, LR: 2.74e-04, Loss: 0.002936, Data Loss: 0.000479, Physics Loss (Raw): 2.402980, Scaled Physics Loss: 0.002403, Known Points Loss (Raw): 0.000113, Scaled Known Points Loss: 0.000054\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.4745\n", + " k1a: 5.6006 (True: 5.0), k1b: 9.7799 (True: 15.0)\n", + " k2a: 11.5682 (True: 10.0), k2b: 3.1699 (True: 2.0)\n", + " k3: 7.8024 (True: 7.0), t_switch: 2.3607 (True: 2.5)\n", + "Epoch 5400/20000, LR: 2.39e-04, Loss: 0.002796, Data Loss: 0.000478, Physics Loss (Raw): 2.278167, Scaled Physics Loss: 0.002278, Known Points Loss (Raw): 0.000048, Scaled Known Points Loss: 0.000039\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.8171\n", + " k1a: 5.5686 (True: 5.0), k1b: 9.8273 (True: 15.0)\n", + " k2a: 11.5712 (True: 10.0), k2b: 3.1325 (True: 2.0)\n", + " k3: 7.7779 (True: 7.0), t_switch: 2.3606 (True: 2.5)\n", + "Epoch 5600/20000, LR: 2.07e-04, Loss: 0.003264, Data Loss: 0.000428, Physics Loss (Raw): 2.810164, Scaled Physics Loss: 0.002810, Known Points Loss (Raw): 0.000017, Scaled Known Points Loss: 0.000026\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 1.5432\n", + " k1a: 5.5409 (True: 5.0), k1b: 9.8686 (True: 15.0)\n", + " k2a: 11.5712 (True: 10.0), k2b: 3.0998 (True: 2.0)\n", + " k3: 7.7543 (True: 7.0), t_switch: 2.3582 (True: 2.5)\n", + "Epoch 5800/20000, LR: 1.76e-04, Loss: 0.002638, Data Loss: 0.000362, Physics Loss (Raw): 2.257511, Scaled Physics Loss: 0.002258, Known Points Loss (Raw): 0.000018, Scaled Known Points Loss: 0.000019\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 1.0201\n", + " k1a: 5.5165 (True: 5.0), k1b: 9.9040 (True: 15.0)\n", + " k2a: 11.5695 (True: 10.0), k2b: 3.0715 (True: 2.0)\n", + " k3: 7.7336 (True: 7.0), t_switch: 2.3611 (True: 2.5)\n", + "Epoch 6000/20000, LR: 1.47e-04, Loss: 0.002421, Data Loss: 0.000387, Physics Loss (Raw): 2.011803, Scaled Physics Loss: 0.002012, Known Points Loss (Raw): 0.000021, Scaled Known Points Loss: 0.000022\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 1.0319\n", + " k1a: 5.4964 (True: 5.0), k1b: 9.9342 (True: 15.0)\n", + " k2a: 11.5663 (True: 10.0), k2b: 3.0474 (True: 2.0)\n", + " k3: 7.7176 (True: 7.0), t_switch: 2.3625 (True: 2.5)\n", + "Epoch 6200/20000, LR: 1.21e-04, Loss: 0.002712, Data Loss: 0.000411, Physics Loss (Raw): 2.270602, Scaled Physics Loss: 0.002271, Known Points Loss (Raw): 0.000011, Scaled Known Points Loss: 0.000030\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 2.7121\n", + " k1a: 5.4791 (True: 5.0), k1b: 9.9591 (True: 15.0)\n", + " k2a: 11.5620 (True: 10.0), k2b: 3.0273 (True: 2.0)\n", + " k3: 7.7034 (True: 7.0), t_switch: 2.3633 (True: 2.5)\n", + "Epoch 6400/20000, LR: 9.63e-05, Loss: 0.002757, Data Loss: 0.000375, Physics Loss (Raw): 2.365215, Scaled Physics Loss: 0.002365, Known Points Loss (Raw): 0.000008, Scaled Known Points Loss: 0.000017\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 1.9797\n", + " k1a: 5.4655 (True: 5.0), k1b: 9.9798 (True: 15.0)\n", + " k2a: 11.5565 (True: 10.0), k2b: 3.0107 (True: 2.0)\n", + " k3: 7.6914 (True: 7.0), t_switch: 2.3644 (True: 2.5)\n", + "Epoch 6600/20000, LR: 7.45e-05, Loss: 0.002582, Data Loss: 0.000348, Physics Loss (Raw): 2.224030, Scaled Physics Loss: 0.002224, Known Points Loss (Raw): 0.000008, Scaled Known Points Loss: 0.000011\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 1.2742\n", + " k1a: 5.4539 (True: 5.0), k1b: 9.9962 (True: 15.0)\n", + " k2a: 11.5520 (True: 10.0), k2b: 2.9973 (True: 2.0)\n", + " k3: 7.6819 (True: 7.0), t_switch: 2.3644 (True: 2.5)\n", + "Epoch 6800/20000, LR: 5.54e-05, Loss: 0.002601, Data Loss: 0.000363, Physics Loss (Raw): 2.230903, Scaled Physics Loss: 0.002231, Known Points Loss (Raw): 0.000003, Scaled Known Points Loss: 0.000007\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 2.2631\n", + " k1a: 5.4449 (True: 5.0), k1b: 10.0085 (True: 15.0)\n", + " k2a: 11.5480 (True: 10.0), k2b: 2.9870 (True: 2.0)\n", + " k3: 7.6746 (True: 7.0), t_switch: 2.3650 (True: 2.5)\n", + "Epoch 7000/20000, LR: 3.89e-05, Loss: 0.002364, Data Loss: 0.000339, Physics Loss (Raw): 2.019464, Scaled Physics Loss: 0.002019, Known Points Loss (Raw): 0.000001, Scaled Known Points Loss: 0.000006\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 4.6604\n", + " k1a: 5.4380 (True: 5.0), k1b: 10.0175 (True: 15.0)\n", + " k2a: 11.5450 (True: 10.0), k2b: 2.9795 (True: 2.0)\n", + " k3: 7.6693 (True: 7.0), t_switch: 2.3643 (True: 2.5)\n", + "Epoch 7200/20000, LR: 2.54e-05, Loss: 0.002340, Data Loss: 0.000343, Physics Loss (Raw): 1.993709, Scaled Physics Loss: 0.001994, Known Points Loss (Raw): 0.000000, Scaled Known Points Loss: 0.000003\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 6.9686\n", + " k1a: 5.4335 (True: 5.0), k1b: 10.0236 (True: 15.0)\n", + " k2a: 11.5423 (True: 10.0), k2b: 2.9743 (True: 2.0)\n", + " k3: 7.6661 (True: 7.0), t_switch: 2.3640 (True: 2.5)\n", + "Epoch 7400/20000, LR: 1.48e-05, Loss: 0.002805, Data Loss: 0.000354, Physics Loss (Raw): 2.448176, Scaled Physics Loss: 0.002448, Known Points Loss (Raw): 0.000000, Scaled Known Points Loss: 0.000003\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 6.0051\n", + " k1a: 5.4304 (True: 5.0), k1b: 10.0275 (True: 15.0)\n", + " k2a: 11.5407 (True: 10.0), k2b: 2.9709 (True: 2.0)\n", + " k3: 7.6639 (True: 7.0), t_switch: 2.3639 (True: 2.5)\n", + "Epoch 7600/20000, LR: 7.12e-06, Loss: 0.002503, Data Loss: 0.000341, Physics Loss (Raw): 2.160904, Scaled Physics Loss: 0.002161, Known Points Loss (Raw): 0.000000, Scaled Known Points Loss: 0.000001\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 28.9585\n", + " k1a: 5.4287 (True: 5.0), k1b: 10.0296 (True: 15.0)\n", + " k2a: 11.5398 (True: 10.0), k2b: 2.9691 (True: 2.0)\n", + " k3: 7.6626 (True: 7.0), t_switch: 2.3638 (True: 2.5)\n", + "Epoch 7800/20000, LR: 2.52e-06, Loss: 0.002391, Data Loss: 0.000346, Physics Loss (Raw): 2.044631, Scaled Physics Loss: 0.002045, Known Points Loss (Raw): 0.000000, Scaled Known Points Loss: 0.000001\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 68.7804\n", + " k1a: 5.4279 (True: 5.0), k1b: 10.0305 (True: 15.0)\n", + " k2a: 11.5394 (True: 10.0), k2b: 2.9683 (True: 2.0)\n", + " k3: 7.6619 (True: 7.0), t_switch: 2.3638 (True: 2.5)\n", + "Epoch 8000/20000, LR: 1.00e-06, Loss: 0.002664, Data Loss: 0.000347, Physics Loss (Raw): 2.316840, Scaled Physics Loss: 0.002317, Known Points Loss (Raw): 0.000000, Scaled Known Points Loss: 0.000001\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 47.0069\n", + " k1a: 5.4277 (True: 5.0), k1b: 10.0308 (True: 15.0)\n", + " k2a: 11.5392 (True: 10.0), k2b: 2.9681 (True: 2.0)\n", + " k3: 7.6617 (True: 7.0), t_switch: 2.3638 (True: 2.5)\n", + "Epoch 8200/20000, LR: 2.56e-06, Loss: 0.002579, Data Loss: 0.000349, Physics Loss (Raw): 2.229674, Scaled Physics Loss: 0.002230, Known Points Loss (Raw): 0.000000, Scaled Known Points Loss: 0.000001\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 33.6855\n", + " k1a: 5.4274 (True: 5.0), k1b: 10.0310 (True: 15.0)\n", + " k2a: 11.5391 (True: 10.0), k2b: 2.9678 (True: 2.0)\n", + " k3: 7.6615 (True: 7.0), t_switch: 2.3638 (True: 2.5)\n", + "Epoch 8400/20000, LR: 7.18e-06, Loss: 0.002516, Data Loss: 0.000344, Physics Loss (Raw): 2.170912, Scaled Physics Loss: 0.002171, Known Points Loss (Raw): 0.000000, Scaled Known Points Loss: 0.000001\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 27.2497\n", + " k1a: 5.4266 (True: 5.0), k1b: 10.0320 (True: 15.0)\n", + " k2a: 11.5386 (True: 10.0), k2b: 2.9670 (True: 2.0)\n", + " k3: 7.6609 (True: 7.0), t_switch: 2.3638 (True: 2.5)\n", + "Epoch 8600/20000, LR: 1.48e-05, Loss: 0.002471, Data Loss: 0.000349, Physics Loss (Raw): 2.118958, Scaled Physics Loss: 0.002119, Known Points Loss (Raw): 0.000000, Scaled Known Points Loss: 0.000003\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 8.3124\n", + " k1a: 5.4249 (True: 5.0), k1b: 10.0341 (True: 15.0)\n", + " k2a: 11.5372 (True: 10.0), k2b: 2.9650 (True: 2.0)\n", + " k3: 7.6595 (True: 7.0), t_switch: 2.3637 (True: 2.5)\n", + "Epoch 8800/20000, LR: 2.55e-05, Loss: 0.002407, Data Loss: 0.000337, Physics Loss (Raw): 2.066626, Scaled Physics Loss: 0.002067, Known Points Loss (Raw): 0.000001, Scaled Known Points Loss: 0.000004\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 4.6505\n", + " k1a: 5.4213 (True: 5.0), k1b: 10.0380 (True: 15.0)\n", + " k2a: 11.5350 (True: 10.0), k2b: 2.9614 (True: 2.0)\n", + " k3: 7.6567 (True: 7.0), t_switch: 2.3641 (True: 2.5)\n", + "Epoch 9000/20000, LR: 3.91e-05, Loss: 0.002546, Data Loss: 0.000321, Physics Loss (Raw): 2.218737, Scaled Physics Loss: 0.002219, Known Points Loss (Raw): 0.000002, Scaled Known Points Loss: 0.000007\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 2.7904\n", + " k1a: 5.4157 (True: 5.0), k1b: 10.0444 (True: 15.0)\n", + " k2a: 11.5308 (True: 10.0), k2b: 2.9556 (True: 2.0)\n", + " k3: 7.6524 (True: 7.0), t_switch: 2.3642 (True: 2.5)\n", + "Epoch 9200/20000, LR: 5.55e-05, Loss: 0.002435, Data Loss: 0.000347, Physics Loss (Raw): 2.077538, Scaled Physics Loss: 0.002078, Known Points Loss (Raw): 0.000007, Scaled Known Points Loss: 0.000010\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 1.5446\n", + " k1a: 5.4077 (True: 5.0), k1b: 10.0537 (True: 15.0)\n", + " k2a: 11.5243 (True: 10.0), k2b: 2.9471 (True: 2.0)\n", + " k3: 7.6461 (True: 7.0), t_switch: 2.3651 (True: 2.5)\n", + "Epoch 9400/20000, LR: 7.47e-05, Loss: 0.002534, Data Loss: 0.000329, Physics Loss (Raw): 2.193983, Scaled Physics Loss: 0.002194, Known Points Loss (Raw): 0.000007, Scaled Known Points Loss: 0.000011\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 1.6225\n", + " k1a: 5.3966 (True: 5.0), k1b: 10.0665 (True: 15.0)\n", + " k2a: 11.5151 (True: 10.0), k2b: 2.9355 (True: 2.0)\n", + " k3: 7.6381 (True: 7.0), t_switch: 2.3643 (True: 2.5)\n", + "Epoch 9600/20000, LR: 9.65e-05, Loss: 0.002607, Data Loss: 0.000331, Physics Loss (Raw): 2.261416, Scaled Physics Loss: 0.002261, Known Points Loss (Raw): 0.000017, Scaled Known Points Loss: 0.000015\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.8666\n", + " k1a: 5.3817 (True: 5.0), k1b: 10.0834 (True: 15.0)\n", + " k2a: 11.5032 (True: 10.0), k2b: 2.9203 (True: 2.0)\n", + " k3: 7.6274 (True: 7.0), t_switch: 2.3639 (True: 2.5)\n", + "Epoch 9800/20000, LR: 1.21e-04, Loss: 0.002444, Data Loss: 0.000365, Physics Loss (Raw): 2.063265, Scaled Physics Loss: 0.002063, Known Points Loss (Raw): 0.000006, Scaled Known Points Loss: 0.000016\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 2.8046\n", + " k1a: 5.3641 (True: 5.0), k1b: 10.1048 (True: 15.0)\n", + " k2a: 11.4849 (True: 10.0), k2b: 2.9010 (True: 2.0)\n", + " k3: 7.6124 (True: 7.0), t_switch: 2.3659 (True: 2.5)\n", + "Epoch 10000/20000, LR: 1.47e-04, Loss: 0.002383, Data Loss: 0.000294, Physics Loss (Raw): 2.073872, Scaled Physics Loss: 0.002074, Known Points Loss (Raw): 0.000011, Scaled Known Points Loss: 0.000015\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 1.3281\n", + " k1a: 5.3411 (True: 5.0), k1b: 10.1309 (True: 15.0)\n", + " k2a: 11.4647 (True: 10.0), k2b: 2.8774 (True: 2.0)\n", + " k3: 7.5925 (True: 7.0), t_switch: 2.3665 (True: 2.5)\n", + "Epoch 10200/20000, LR: 1.76e-04, Loss: 0.002315, Data Loss: 0.000285, Physics Loss (Raw): 2.002245, Scaled Physics Loss: 0.002002, Known Points Loss (Raw): 0.000050, Scaled Known Points Loss: 0.000027\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.5402\n", + " k1a: 5.3162 (True: 5.0), k1b: 10.1625 (True: 15.0)\n", + " k2a: 11.4350 (True: 10.0), k2b: 2.8498 (True: 2.0)\n", + " k3: 7.5724 (True: 7.0), t_switch: 2.3680 (True: 2.5)\n", + "Epoch 10400/20000, LR: 2.07e-04, Loss: 0.002328, Data Loss: 0.000309, Physics Loss (Raw): 1.993801, Scaled Physics Loss: 0.001994, Known Points Loss (Raw): 0.000032, Scaled Known Points Loss: 0.000025\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.7921\n", + " k1a: 5.2864 (True: 5.0), k1b: 10.1997 (True: 15.0)\n", + " k2a: 11.4010 (True: 10.0), k2b: 2.8181 (True: 2.0)\n", + " k3: 7.5497 (True: 7.0), t_switch: 2.3674 (True: 2.5)\n", + "Epoch 10600/20000, LR: 2.40e-04, Loss: 0.002307, Data Loss: 0.000288, Physics Loss (Raw): 1.983875, Scaled Physics Loss: 0.001984, Known Points Loss (Raw): 0.000053, Scaled Known Points Loss: 0.000035\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.6638\n", + " k1a: 5.2527 (True: 5.0), k1b: 10.2427 (True: 15.0)\n", + " k2a: 11.3605 (True: 10.0), k2b: 2.7820 (True: 2.0)\n", + " k3: 7.5247 (True: 7.0), t_switch: 2.3656 (True: 2.5)\n", + "Epoch 10800/20000, LR: 2.74e-04, Loss: 0.002656, Data Loss: 0.000267, Physics Loss (Raw): 2.361826, Scaled Physics Loss: 0.002362, Known Points Loss (Raw): 0.000100, Scaled Known Points Loss: 0.000026\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.2648\n", + " k1a: 5.2124 (True: 5.0), k1b: 10.2920 (True: 15.0)\n", + " k2a: 11.3176 (True: 10.0), k2b: 2.7418 (True: 2.0)\n", + " k3: 7.4976 (True: 7.0), t_switch: 2.3689 (True: 2.5)\n", + "Epoch 11000/20000, LR: 3.10e-04, Loss: 0.002229, Data Loss: 0.000252, Physics Loss (Raw): 1.957136, Scaled Physics Loss: 0.001957, Known Points Loss (Raw): 0.000016, Scaled Known Points Loss: 0.000019\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 1.2370\n", + " k1a: 5.1688 (True: 5.0), k1b: 10.3482 (True: 15.0)\n", + " k2a: 11.2682 (True: 10.0), k2b: 2.6978 (True: 2.0)\n", + " k3: 7.4615 (True: 7.0), t_switch: 2.3684 (True: 2.5)\n", + "Epoch 11200/20000, LR: 3.46e-04, Loss: 0.001870, Data Loss: 0.000256, Physics Loss (Raw): 1.582697, Scaled Physics Loss: 0.001583, Known Points Loss (Raw): 0.000050, Scaled Known Points Loss: 0.000031\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.6228\n", + " k1a: 5.1244 (True: 5.0), k1b: 10.4112 (True: 15.0)\n", + " k2a: 11.2062 (True: 10.0), k2b: 2.6511 (True: 2.0)\n", + " k3: 7.4255 (True: 7.0), t_switch: 2.3721 (True: 2.5)\n", + "Epoch 11400/20000, LR: 3.84e-04, Loss: 0.001825, Data Loss: 0.000210, Physics Loss (Raw): 1.580692, Scaled Physics Loss: 0.001581, Known Points Loss (Raw): 0.000134, Scaled Known Points Loss: 0.000034\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.2541\n", + " k1a: 5.0787 (True: 5.0), k1b: 10.4801 (True: 15.0)\n", + " k2a: 11.1360 (True: 10.0), k2b: 2.6022 (True: 2.0)\n", + " k3: 7.3935 (True: 7.0), t_switch: 2.3737 (True: 2.5)\n", + "Epoch 11600/20000, LR: 4.23e-04, Loss: 0.002015, Data Loss: 0.000216, Physics Loss (Raw): 1.759759, Scaled Physics Loss: 0.001760, Known Points Loss (Raw): 0.000402, Scaled Known Points Loss: 0.000039\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0978\n", + " k1a: 5.0341 (True: 5.0), k1b: 10.5560 (True: 15.0)\n", + " k2a: 11.0539 (True: 10.0), k2b: 2.5519 (True: 2.0)\n", + " k3: 7.3628 (True: 7.0), t_switch: 2.3751 (True: 2.5)\n", + "Epoch 11800/20000, LR: 4.62e-04, Loss: 0.001870, Data Loss: 0.000211, Physics Loss (Raw): 1.633698, Scaled Physics Loss: 0.001634, Known Points Loss (Raw): 0.000100, Scaled Known Points Loss: 0.000025\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.2502\n", + " k1a: 4.9851 (True: 5.0), k1b: 10.6392 (True: 15.0)\n", + " k2a: 10.9717 (True: 10.0), k2b: 2.4994 (True: 2.0)\n", + " k3: 7.3264 (True: 7.0), t_switch: 2.3771 (True: 2.5)\n", + "Epoch 12000/20000, LR: 5.01e-04, Loss: 0.001811, Data Loss: 0.000193, Physics Loss (Raw): 1.595574, Scaled Physics Loss: 0.001596, Known Points Loss (Raw): 0.000083, Scaled Known Points Loss: 0.000022\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.2674\n", + " k1a: 4.9349 (True: 5.0), k1b: 10.7287 (True: 15.0)\n", + " k2a: 10.8857 (True: 10.0), k2b: 2.4476 (True: 2.0)\n", + " k3: 7.2894 (True: 7.0), t_switch: 2.3795 (True: 2.5)\n", + "Epoch 12200/20000, LR: 5.40e-04, Loss: 0.001659, Data Loss: 0.000169, Physics Loss (Raw): 1.474811, Scaled Physics Loss: 0.001475, Known Points Loss (Raw): 0.000135, Scaled Known Points Loss: 0.000015\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.1134\n", + " k1a: 4.8787 (True: 5.0), k1b: 10.8250 (True: 15.0)\n", + " k2a: 10.8007 (True: 10.0), k2b: 2.3968 (True: 2.0)\n", + " k3: 7.2457 (True: 7.0), t_switch: 2.3750 (True: 2.5)\n", + "Epoch 12400/20000, LR: 5.79e-04, Loss: 0.001653, Data Loss: 0.000175, Physics Loss (Raw): 1.445333, Scaled Physics Loss: 0.001445, Known Points Loss (Raw): 0.000235, Scaled Known Points Loss: 0.000033\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.1384\n", + " k1a: 4.8194 (True: 5.0), k1b: 10.9272 (True: 15.0)\n", + " k2a: 10.7093 (True: 10.0), k2b: 2.3471 (True: 2.0)\n", + " k3: 7.2068 (True: 7.0), t_switch: 2.3792 (True: 2.5)\n", + "Epoch 12600/20000, LR: 6.17e-04, Loss: 0.001539, Data Loss: 0.000182, Physics Loss (Raw): 1.252276, Scaled Physics Loss: 0.001252, Known Points Loss (Raw): 0.001169, Scaled Known Points Loss: 0.000104\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0894\n", + " k1a: 4.7526 (True: 5.0), k1b: 11.0345 (True: 15.0)\n", + " k2a: 10.6215 (True: 10.0), k2b: 2.3035 (True: 2.0)\n", + " k3: 7.1735 (True: 7.0), t_switch: 2.3769 (True: 2.5)\n", + "Epoch 12800/20000, LR: 6.55e-04, Loss: 0.001372, Data Loss: 0.000152, Physics Loss (Raw): 1.197331, Scaled Physics Loss: 0.001197, Known Points Loss (Raw): 0.000094, Scaled Known Points Loss: 0.000023\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.2478\n", + " k1a: 4.6963 (True: 5.0), k1b: 11.1478 (True: 15.0)\n", + " k2a: 10.5175 (True: 10.0), k2b: 2.2647 (True: 2.0)\n", + " k3: 7.1431 (True: 7.0), t_switch: 2.3845 (True: 2.5)\n", + "Epoch 13000/20000, LR: 6.92e-04, Loss: 0.001357, Data Loss: 0.000135, Physics Loss (Raw): 1.208420, Scaled Physics Loss: 0.001208, Known Points Loss (Raw): 0.000073, Scaled Known Points Loss: 0.000013\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.1819\n", + " k1a: 4.6664 (True: 5.0), k1b: 11.2672 (True: 15.0)\n", + " k2a: 10.3919 (True: 10.0), k2b: 2.2310 (True: 2.0)\n", + " k3: 7.1285 (True: 7.0), t_switch: 2.3930 (True: 2.5)\n", + "Epoch 13200/20000, LR: 7.27e-04, Loss: 0.001412, Data Loss: 0.000125, Physics Loss (Raw): 1.270473, Scaled Physics Loss: 0.001270, Known Points Loss (Raw): 0.000172, Scaled Known Points Loss: 0.000017\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0980\n", + " k1a: 4.6072 (True: 5.0), k1b: 11.3913 (True: 15.0)\n", + " k2a: 10.2978 (True: 10.0), k2b: 2.1996 (True: 2.0)\n", + " k3: 7.1019 (True: 7.0), t_switch: 2.3983 (True: 2.5)\n", + "Epoch 13400/20000, LR: 7.62e-04, Loss: 0.001172, Data Loss: 0.000133, Physics Loss (Raw): 1.016387, Scaled Physics Loss: 0.001016, Known Points Loss (Raw): 0.000123, Scaled Known Points Loss: 0.000022\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.1805\n", + " k1a: 4.5943 (True: 5.0), k1b: 11.5217 (True: 15.0)\n", + " k2a: 10.1720 (True: 10.0), k2b: 2.1764 (True: 2.0)\n", + " k3: 7.0898 (True: 7.0), t_switch: 2.4059 (True: 2.5)\n", + "Epoch 13600/20000, LR: 7.94e-04, Loss: 0.001051, Data Loss: 0.000128, Physics Loss (Raw): 0.848504, Scaled Physics Loss: 0.000849, Known Points Loss (Raw): 0.000862, Scaled Known Points Loss: 0.000075\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0865\n", + " k1a: 4.5500 (True: 5.0), k1b: 11.6545 (True: 15.0)\n", + " k2a: 10.0847 (True: 10.0), k2b: 2.1595 (True: 2.0)\n", + " k3: 7.0764 (True: 7.0), t_switch: 2.4067 (True: 2.5)\n", + "Epoch 13800/20000, LR: 8.25e-04, Loss: 0.001066, Data Loss: 0.000099, Physics Loss (Raw): 0.944131, Scaled Physics Loss: 0.000944, Known Points Loss (Raw): 0.000299, Scaled Known Points Loss: 0.000023\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0763\n", + " k1a: 4.5117 (True: 5.0), k1b: 11.7913 (True: 15.0)\n", + " k2a: 9.9945 (True: 10.0), k2b: 2.1467 (True: 2.0)\n", + " k3: 7.0641 (True: 7.0), t_switch: 2.4101 (True: 2.5)\n", + "Epoch 14000/20000, LR: 8.54e-04, Loss: 0.001020, Data Loss: 0.000120, Physics Loss (Raw): 0.793438, Scaled Physics Loss: 0.000793, Known Points Loss (Raw): 0.001583, Scaled Known Points Loss: 0.000106\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0672\n", + " k1a: 4.4649 (True: 5.0), k1b: 11.9297 (True: 15.0)\n", + " k2a: 9.9119 (True: 10.0), k2b: 2.1356 (True: 2.0)\n", + " k3: 7.0533 (True: 7.0), t_switch: 2.4135 (True: 2.5)\n", + "Epoch 14200/20000, LR: 8.80e-04, Loss: 0.000855, Data Loss: 0.000084, Physics Loss (Raw): 0.747468, Scaled Physics Loss: 0.000747, Known Points Loss (Raw): 0.000571, Scaled Known Points Loss: 0.000023\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0412\n", + " k1a: 4.4040 (True: 5.0), k1b: 12.0697 (True: 15.0)\n", + " k2a: 9.8398 (True: 10.0), k2b: 2.1239 (True: 2.0)\n", + " k3: 7.0448 (True: 7.0), t_switch: 2.4156 (True: 2.5)\n", + "Epoch 14400/20000, LR: 9.05e-04, Loss: 0.000841, Data Loss: 0.000104, Physics Loss (Raw): 0.710430, Scaled Physics Loss: 0.000710, Known Points Loss (Raw): 0.000203, Scaled Known Points Loss: 0.000027\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.1340\n", + " k1a: 4.3820 (True: 5.0), k1b: 12.2136 (True: 15.0)\n", + " k2a: 9.7460 (True: 10.0), k2b: 2.1206 (True: 2.0)\n", + " k3: 7.0442 (True: 7.0), t_switch: 2.4156 (True: 2.5)\n", + "Epoch 14600/20000, LR: 9.26e-04, Loss: 0.000597, Data Loss: 0.000097, Physics Loss (Raw): 0.475829, Scaled Physics Loss: 0.000476, Known Points Loss (Raw): 0.000133, Scaled Known Points Loss: 0.000024\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.1816\n", + " k1a: 4.3204 (True: 5.0), k1b: 12.3559 (True: 15.0)\n", + " k2a: 9.6813 (True: 10.0), k2b: 2.1144 (True: 2.0)\n", + " k3: 7.0378 (True: 7.0), t_switch: 2.4198 (True: 2.5)\n", + "Epoch 14800/20000, LR: 9.46e-04, Loss: 0.000605, Data Loss: 0.000068, Physics Loss (Raw): 0.516310, Scaled Physics Loss: 0.000516, Known Points Loss (Raw): 0.000100, Scaled Known Points Loss: 0.000021\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.2060\n", + " k1a: 4.2891 (True: 5.0), k1b: 12.4984 (True: 15.0)\n", + " k2a: 9.5893 (True: 10.0), k2b: 2.1125 (True: 2.0)\n", + " k3: 7.0328 (True: 7.0), t_switch: 2.4241 (True: 2.5)\n", + "Epoch 15000/20000, LR: 9.62e-04, Loss: 0.000559, Data Loss: 0.000076, Physics Loss (Raw): 0.443574, Scaled Physics Loss: 0.000444, Known Points Loss (Raw): 0.000502, Scaled Known Points Loss: 0.000039\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0779\n", + " k1a: 4.2503 (True: 5.0), k1b: 12.6407 (True: 15.0)\n", + " k2a: 9.5232 (True: 10.0), k2b: 2.1071 (True: 2.0)\n", + " k3: 7.0299 (True: 7.0), t_switch: 2.4209 (True: 2.5)\n", + "Epoch 15200/20000, LR: 9.76e-04, Loss: 0.000535, Data Loss: 0.000064, Physics Loss (Raw): 0.455859, Scaled Physics Loss: 0.000456, Known Points Loss (Raw): 0.000263, Scaled Known Points Loss: 0.000015\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0582\n", + " k1a: 4.2072 (True: 5.0), k1b: 12.7805 (True: 15.0)\n", + " k2a: 9.4661 (True: 10.0), k2b: 2.0988 (True: 2.0)\n", + " k3: 7.0219 (True: 7.0), t_switch: 2.4273 (True: 2.5)\n", + "Epoch 15400/20000, LR: 9.86e-04, Loss: 0.000444, Data Loss: 0.000055, Physics Loss (Raw): 0.379614, Scaled Physics Loss: 0.000380, Known Points Loss (Raw): 0.000084, Scaled Known Points Loss: 0.000009\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.1081\n", + " k1a: 4.2160 (True: 5.0), k1b: 12.9219 (True: 15.0)\n", + " k2a: 9.3780 (True: 10.0), k2b: 2.0947 (True: 2.0)\n", + " k3: 7.0269 (True: 7.0), t_switch: 2.4341 (True: 2.5)\n", + "Epoch 15600/20000, LR: 9.94e-04, Loss: 0.000371, Data Loss: 0.000063, Physics Loss (Raw): 0.290056, Scaled Physics Loss: 0.000290, Known Points Loss (Raw): 0.000459, Scaled Known Points Loss: 0.000018\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0389\n", + " k1a: 4.1716 (True: 5.0), k1b: 13.0580 (True: 15.0)\n", + " k2a: 9.3533 (True: 10.0), k2b: 2.0922 (True: 2.0)\n", + " k3: 7.0196 (True: 7.0), t_switch: 2.4270 (True: 2.5)\n", + "Epoch 15800/20000, LR: 9.98e-04, Loss: 0.000382, Data Loss: 0.000048, Physics Loss (Raw): 0.315456, Scaled Physics Loss: 0.000315, Known Points Loss (Raw): 0.000252, Scaled Known Points Loss: 0.000018\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0729\n", + " k1a: 4.1756 (True: 5.0), k1b: 13.1921 (True: 15.0)\n", + " k2a: 9.2826 (True: 10.0), k2b: 2.0853 (True: 2.0)\n", + " k3: 7.0175 (True: 7.0), t_switch: 2.4418 (True: 2.5)\n", + "Epoch 16000/20000, LR: 1.00e-03, Loss: 0.000345, Data Loss: 0.000049, Physics Loss (Raw): 0.260680, Scaled Physics Loss: 0.000261, Known Points Loss (Raw): 0.000598, Scaled Known Points Loss: 0.000035\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0591\n", + " k1a: 4.1704 (True: 5.0), k1b: 13.3239 (True: 15.0)\n", + " k2a: 9.2605 (True: 10.0), k2b: 2.0783 (True: 2.0)\n", + " k3: 7.0132 (True: 7.0), t_switch: 2.4402 (True: 2.5)\n", + "Epoch 16200/20000, LR: 9.98e-04, Loss: 0.000287, Data Loss: 0.000040, Physics Loss (Raw): 0.234767, Scaled Physics Loss: 0.000235, Known Points Loss (Raw): 0.000116, Scaled Known Points Loss: 0.000011\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0970\n", + " k1a: 4.1550 (True: 5.0), k1b: 13.4495 (True: 15.0)\n", + " k2a: 9.2376 (True: 10.0), k2b: 2.0729 (True: 2.0)\n", + " k3: 7.0149 (True: 7.0), t_switch: 2.4434 (True: 2.5)\n", + "Epoch 16400/20000, LR: 9.94e-04, Loss: 0.000258, Data Loss: 0.000037, Physics Loss (Raw): 0.202454, Scaled Physics Loss: 0.000202, Known Points Loss (Raw): 0.000175, Scaled Known Points Loss: 0.000019\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.1072\n", + " k1a: 4.2216 (True: 5.0), k1b: 13.5749 (True: 15.0)\n", + " k2a: 9.1705 (True: 10.0), k2b: 2.0688 (True: 2.0)\n", + " k3: 7.0167 (True: 7.0), t_switch: 2.4550 (True: 2.5)\n", + "Epoch 16600/20000, LR: 9.86e-04, Loss: 0.000255, Data Loss: 0.000044, Physics Loss (Raw): 0.177896, Scaled Physics Loss: 0.000178, Known Points Loss (Raw): 0.000148, Scaled Known Points Loss: 0.000033\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.2248\n", + " k1a: 4.2348 (True: 5.0), k1b: 13.6959 (True: 15.0)\n", + " k2a: 9.2106 (True: 10.0), k2b: 2.0641 (True: 2.0)\n", + " k3: 7.0073 (True: 7.0), t_switch: 2.4538 (True: 2.5)\n", + "Epoch 16800/20000, LR: 9.75e-04, Loss: 0.000151, Data Loss: 0.000029, Physics Loss (Raw): 0.115213, Scaled Physics Loss: 0.000115, Known Points Loss (Raw): 0.000099, Scaled Known Points Loss: 0.000007\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0727\n", + " k1a: 4.2184 (True: 5.0), k1b: 13.8058 (True: 15.0)\n", + " k2a: 9.2308 (True: 10.0), k2b: 2.0570 (True: 2.0)\n", + " k3: 7.0077 (True: 7.0), t_switch: 2.4508 (True: 2.5)\n", + "Epoch 17000/20000, LR: 9.62e-04, Loss: 0.000145, Data Loss: 0.000028, Physics Loss (Raw): 0.108026, Scaled Physics Loss: 0.000108, Known Points Loss (Raw): 0.000126, Scaled Known Points Loss: 0.000009\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0717\n", + " k1a: 4.2114 (True: 5.0), k1b: 13.9096 (True: 15.0)\n", + " k2a: 9.2234 (True: 10.0), k2b: 2.0540 (True: 2.0)\n", + " k3: 7.0032 (True: 7.0), t_switch: 2.4524 (True: 2.5)\n", + "Epoch 17200/20000, LR: 9.45e-04, Loss: 0.000149, Data Loss: 0.000027, Physics Loss (Raw): 0.113770, Scaled Physics Loss: 0.000114, Known Points Loss (Raw): 0.000079, Scaled Known Points Loss: 0.000009\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.1086\n", + " k1a: 4.1686 (True: 5.0), k1b: 14.0010 (True: 15.0)\n", + " k2a: 9.2210 (True: 10.0), k2b: 2.0485 (True: 2.0)\n", + " k3: 6.9940 (True: 7.0), t_switch: 2.4525 (True: 2.5)\n", + "Epoch 17400/20000, LR: 9.26e-04, Loss: 0.000126, Data Loss: 0.000022, Physics Loss (Raw): 0.099886, Scaled Physics Loss: 0.000100, Known Points Loss (Raw): 0.000072, Scaled Known Points Loss: 0.000004\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0607\n", + " k1a: 4.1477 (True: 5.0), k1b: 14.0877 (True: 15.0)\n", + " k2a: 9.1880 (True: 10.0), k2b: 2.0437 (True: 2.0)\n", + " k3: 6.9922 (True: 7.0), t_switch: 2.4531 (True: 2.5)\n", + "Epoch 17600/20000, LR: 9.04e-04, Loss: 0.000115, Data Loss: 0.000020, Physics Loss (Raw): 0.078433, Scaled Physics Loss: 0.000078, Known Points Loss (Raw): 0.000402, Scaled Known Points Loss: 0.000017\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0411\n", + " k1a: 4.1788 (True: 5.0), k1b: 14.1732 (True: 15.0)\n", + " k2a: 9.1297 (True: 10.0), k2b: 2.0412 (True: 2.0)\n", + " k3: 6.9980 (True: 7.0), t_switch: 2.4636 (True: 2.5)\n", + "Epoch 17800/20000, LR: 8.80e-04, Loss: 0.000105, Data Loss: 0.000015, Physics Loss (Raw): 0.088068, Scaled Physics Loss: 0.000088, Known Points Loss (Raw): 0.000027, Scaled Known Points Loss: 0.000002\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0748\n", + " k1a: 4.1958 (True: 5.0), k1b: 14.2523 (True: 15.0)\n", + " k2a: 9.1349 (True: 10.0), k2b: 2.0384 (True: 2.0)\n", + " k3: 6.9933 (True: 7.0), t_switch: 2.4610 (True: 2.5)\n", + "Epoch 18000/20000, LR: 8.54e-04, Loss: 0.000078, Data Loss: 0.000018, Physics Loss (Raw): 0.045741, Scaled Physics Loss: 0.000046, Known Points Loss (Raw): 0.000186, Scaled Known Points Loss: 0.000014\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0727\n", + " k1a: 4.2597 (True: 5.0), k1b: 14.3314 (True: 15.0)\n", + " k2a: 9.1028 (True: 10.0), k2b: 2.0353 (True: 2.0)\n", + " k3: 7.0008 (True: 7.0), t_switch: 2.4696 (True: 2.5)\n", + "Epoch 18200/20000, LR: 8.25e-04, Loss: 0.000101, Data Loss: 0.000013, Physics Loss (Raw): 0.081322, Scaled Physics Loss: 0.000081, Known Points Loss (Raw): 0.000128, Scaled Known Points Loss: 0.000006\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0484\n", + " k1a: 4.2929 (True: 5.0), k1b: 14.4030 (True: 15.0)\n", + " k2a: 9.1523 (True: 10.0), k2b: 2.0329 (True: 2.0)\n", + " k3: 6.9956 (True: 7.0), t_switch: 2.4661 (True: 2.5)\n", + "Epoch 18400/20000, LR: 7.94e-04, Loss: 0.000076, Data Loss: 0.000013, Physics Loss (Raw): 0.060551, Scaled Physics Loss: 0.000061, Known Points Loss (Raw): 0.000108, Scaled Known Points Loss: 0.000003\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0278\n", + " k1a: 4.2677 (True: 5.0), k1b: 14.4595 (True: 15.0)\n", + " k2a: 9.2087 (True: 10.0), k2b: 2.0280 (True: 2.0)\n", + " k3: 6.9859 (True: 7.0), t_switch: 2.4639 (True: 2.5)\n", + "Epoch 18600/20000, LR: 7.61e-04, Loss: 0.000066, Data Loss: 0.000012, Physics Loss (Raw): 0.048740, Scaled Physics Loss: 0.000049, Known Points Loss (Raw): 0.000090, Scaled Known Points Loss: 0.000005\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0517\n", + " k1a: 4.2685 (True: 5.0), k1b: 14.5074 (True: 15.0)\n", + " k2a: 9.2119 (True: 10.0), k2b: 2.0268 (True: 2.0)\n", + " k3: 6.9847 (True: 7.0), t_switch: 2.4657 (True: 2.5)\n", + "Epoch 18800/20000, LR: 7.27e-04, Loss: 0.000079, Data Loss: 0.000015, Physics Loss (Raw): 0.057285, Scaled Physics Loss: 0.000057, Known Points Loss (Raw): 0.000066, Scaled Known Points Loss: 0.000007\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.1047\n", + " k1a: 4.2704 (True: 5.0), k1b: 14.5513 (True: 15.0)\n", + " k2a: 9.2047 (True: 10.0), k2b: 2.0232 (True: 2.0)\n", + " k3: 6.9853 (True: 7.0), t_switch: 2.4679 (True: 2.5)\n", + "Epoch 19000/20000, LR: 6.91e-04, Loss: 0.000044, Data Loss: 0.000011, Physics Loss (Raw): 0.026907, Scaled Physics Loss: 0.000027, Known Points Loss (Raw): 0.000137, Scaled Known Points Loss: 0.000007\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0479\n", + " k1a: 4.3012 (True: 5.0), k1b: 14.5951 (True: 15.0)\n", + " k2a: 9.1892 (True: 10.0), k2b: 2.0216 (True: 2.0)\n", + " k3: 6.9850 (True: 7.0), t_switch: 2.4714 (True: 2.5)\n", + "Epoch 19200/20000, LR: 6.55e-04, Loss: 0.000044, Data Loss: 0.000010, Physics Loss (Raw): 0.032140, Scaled Physics Loss: 0.000032, Known Points Loss (Raw): 0.000031, Scaled Known Points Loss: 0.000002\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0651\n", + " k1a: 4.3069 (True: 5.0), k1b: 14.6297 (True: 15.0)\n", + " k2a: 9.2074 (True: 10.0), k2b: 2.0202 (True: 2.0)\n", + " k3: 6.9843 (True: 7.0), t_switch: 2.4706 (True: 2.5)\n", + "Epoch 19400/20000, LR: 6.17e-04, Loss: 0.000053, Data Loss: 0.000009, Physics Loss (Raw): 0.035882, Scaled Physics Loss: 0.000036, Known Points Loss (Raw): 0.000160, Scaled Known Points Loss: 0.000008\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0495\n", + " k1a: 4.3139 (True: 5.0), k1b: 14.6592 (True: 15.0)\n", + " k2a: 9.2136 (True: 10.0), k2b: 2.0192 (True: 2.0)\n", + " k3: 6.9832 (True: 7.0), t_switch: 2.4727 (True: 2.5)\n", + "Epoch 19600/20000, LR: 5.78e-04, Loss: 0.000042, Data Loss: 0.000006, Physics Loss (Raw): 0.035108, Scaled Physics Loss: 0.000035, Known Points Loss (Raw): 0.000017, Scaled Known Points Loss: 0.000001\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0515\n", + " k1a: 4.3261 (True: 5.0), k1b: 14.6861 (True: 15.0)\n", + " k2a: 9.2231 (True: 10.0), k2b: 2.0174 (True: 2.0)\n", + " k3: 6.9840 (True: 7.0), t_switch: 2.4743 (True: 2.5)\n", + "Epoch 19800/20000, LR: 5.39e-04, Loss: 0.000021, Data Loss: 0.000004, Physics Loss (Raw): 0.013793, Scaled Physics Loss: 0.000014, Known Points Loss (Raw): 0.000066, Scaled Known Points Loss: 0.000002\n", + " Adaptive Lambda Physics: 0.0010, Adaptive Lambda Known Points: 0.0354\n", + " k1a: 4.3473 (True: 5.0), k1b: 14.7111 (True: 15.0)\n", + " k2a: 9.2215 (True: 10.0), k2b: 2.0169 (True: 2.0)\n", + " k3: 6.9862 (True: 7.0), t_switch: 2.4795 (True: 2.5)\n", + "\n", + "--- Training Complete ---\n", + "Inferred k1 (before): 4.3750 (True: 5.0)\n", + "Inferred k1 (after): 14.7356 (True: 15.0)\n", + "Inferred k2 (before): 9.2281 (True: 10.0)\n", + "Inferred k2 (after): 2.0152 (True: 2.0)\n", + "Inferred k3: 6.9863 (True: 7.0)\n", + "Inferred t_switch: 2.4787 (True: 2.5)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Training loop\n", + "for epoch in range(epochs):\n", + " optimizer.zero_grad() # Zero gradients once at the beginning of the iteration\n", + "\n", + " # 1. Data Loss\n", + " x_pred_data = pinn(t_data)\n", + " data_loss = torch.mean((x_pred_data - x_data)**2)\n", + "\n", + " # 2. Physics Loss\n", + " t_physics_batch = torch.rand(N_physics_points, 1, device=device) * (time_span[1] - time_span[0]) + time_span[0]\n", + " physics_loss = pinn.compute_physics_loss(t_physics_batch)\n", + "\n", + " # 3. Known Points Loss (New: handles initial and multiple boundary conditions)\n", + " known_points_loss = pinn.compute_known_point_loss(\n", + " t_known_points, x_known_true_tensor, v_known_true_tensor\n", + " )\n", + "\n", + " # --- Adaptive Weighting Logic (Corrected Gradient-Norm Balancing) ---\n", + " # Compute gradients of individual loss terms without zeroing the optimizer's state\n", + " # This captures the raw gradient magnitudes from each loss.\n", + " \n", + " # Get gradients of data_loss w.r.t NN parameters (excluding trainable physics params if desired, but here we take all)\n", + " # Use retain_graph=True for all intermediate grad calls\n", + " data_grads_for_norm = torch.autograd.grad(\n", + " data_loss,\n", + " pinn.parameters(),\n", + " retain_graph=True,\n", + " allow_unused=True # Allow for parameters not touched by this loss\n", + " )\n", + " # Filter out None gradients and calculate norm\n", + " data_grad_norm = torch.sqrt(sum([g.norm()**2 for g in data_grads_for_norm if g is not None]))\n", + " # Handle case where no gradients are present for some reason (though unlikely for NN params)\n", + " if data_grad_norm == 0: data_grad_norm = torch.tensor(1.0, device=device)\n", + "\n", + "\n", + " # Get gradients of physics_loss w.r.t. ALL trainable parameters\n", + " physics_grads_for_norm = torch.autograd.grad(\n", + " physics_loss,\n", + " pinn.parameters(),\n", + " retain_graph=True,\n", + " allow_unused=True # Allow for parameters not touched by this loss\n", + " )\n", + " # Filter out None gradients and calculate norm\n", + " physics_grad_norm = torch.sqrt(sum([g.norm()**2 for g in physics_grads_for_norm if g is not None]))\n", + " # Handle case where no gradients are present for some reason\n", + " if physics_grad_norm == 0: physics_grad_norm = torch.tensor(1.0, device=device)\n", + "\n", + " # Get gradients of known_points_loss w.r.t. ALL trainable parameters\n", + " known_points_grads_for_norm = torch.autograd.grad(\n", + " known_points_loss,\n", + " pinn.parameters(),\n", + " retain_graph=True,\n", + " allow_unused=True # Allow for parameters not touched by this loss\n", + " )\n", + " # Filter out None gradients and calculate norm\n", + " known_points_grad_norm = torch.sqrt(sum([g.norm()**2 for g in known_points_grads_for_norm if g is not None]))\n", + " # Handle case where no gradients are present for some reason\n", + " if known_points_grad_norm == 0: known_points_grad_norm = torch.tensor(1.0, device=device)\n", + "\n", + "\n", + " # Adjust lambda_physics based on gradient magnitudes\n", + " adaptive_lambda_physics = lambda_physics * (data_grad_norm / physics_grad_norm).detach()\n", + " # Clamp the adaptive lambda to prevent it from exploding or vanishing too much\n", + " adaptive_lambda_physics = torch.clamp(adaptive_lambda_physics, 0.001, 10.0) # Example bounds\n", + "\n", + " # Adjust lambda_known_points based on gradient magnitudes\n", + " adaptive_lambda_known_points = lambda_known_points * (data_grad_norm / known_points_grad_norm).detach()\n", + " adaptive_lambda_known_points = torch.clamp(adaptive_lambda_known_points, 0.001, 1000.0) # Can be higher due to strictness of ICs\n", + "\n", + "\n", + " # Combined Loss using the adaptive weight\n", + " total_loss = lambda_data * data_loss + \\\n", + " adaptive_lambda_physics * physics_loss + \\\n", + " adaptive_lambda_known_points * known_points_loss\n", + "\n", + " # Now backpropagate the combined loss for final parameter updates\n", + " total_loss.backward()\n", + " \n", + " # Clip gradients to prevent explosion (common cause of NaNs)\n", + " torch.nn.utils.clip_grad_norm_(pinn.parameters(), max_norm=1.0) # Clip gradients to a max norm of 1.0\n", + "\n", + " optimizer.step()\n", + " scheduler.step() # Update learning rate\n", + "\n", + " history['loss'].append(total_loss.item())\n", + " history['data_loss'].append(data_loss.item())\n", + " history['physics_loss'].append(physics_loss.item())\n", + " history['scaled_physics_loss'].append(adaptive_lambda_physics.item() * physics_loss.item()) # Store scaled value\n", + " history['known_points_loss'].append(known_points_loss.item()) # Store raw known points loss\n", + " history['scaled_known_points_loss'].append(adaptive_lambda_known_points.item() * known_points_loss.item()) # Store scaled known points loss\n", + " history['k1a'].append(pinn.k1a.item())\n", + " history['k1b'].append(pinn.k1b.item())\n", + " history['k2a'].append(pinn.k2a.item())\n", + " history['k2b'].append(pinn.k2b.item())\n", + " history['k3'].append(pinn.k3.item())\n", + " history['t_switch'].append(pinn.t_switch.item())\n", + " history['current_lr'].append(optimizer.param_groups[0]['lr'])\n", + " history['adaptive_lambda_physics'].append(adaptive_lambda_physics.item())\n", + " history['adaptive_lambda_known_points'].append(adaptive_lambda_known_points.item())\n", + "\n", + " if epoch % 200 == 0:\n", + " print(f\"Epoch {epoch}/{epochs}, LR: {optimizer.param_groups[0]['lr']:.2e}, \"\n", + " f\"Loss: {total_loss.item():.6f}, Data Loss: {data_loss.item():.6f}, \"\n", + " f\"Physics Loss (Raw): {physics_loss.item():.6f}, Scaled Physics Loss: {adaptive_lambda_physics.item() * physics_loss.item():.6f}, \"\n", + " f\"Known Points Loss (Raw): {known_points_loss.item():.6f}, Scaled Known Points Loss: {adaptive_lambda_known_points.item() * known_points_loss.item():.6f}\")\n", + " print(f\" Adaptive Lambda Physics: {adaptive_lambda_physics.item():.4f}, Adaptive Lambda Known Points: {adaptive_lambda_known_points.item():.4f}\")\n", + " print(f\" k1a: {pinn.k1a.item():.4f} (True: {true_k1a}), k1b: {pinn.k1b.item():.4f} (True: {true_k1b})\")\n", + " print(f\" k2a: {pinn.k2a.item():.4f} (True: {true_k2a}), k2b: {pinn.k2b.item():.4f} (True: {true_k2b})\")\n", + " print(f\" k3: {pinn.k3.item():.4f} (True: {true_k3}), t_switch: {pinn.t_switch.item():.4f} (True: {true_t_switch})\")\n", + "\n", + "print(\"\\n--- Training Complete ---\")\n", + "print(f\"Inferred k1 (before): {pinn.k1a.item():.4f} (True: {true_k1a})\")\n", + "print(f\"Inferred k1 (after): {pinn.k1b.item():.4f} (True: {true_k1b})\")\n", + "print(f\"Inferred k2 (before): {pinn.k2a.item():.4f} (True: {true_k2a})\")\n", + "print(f\"Inferred k2 (after): {pinn.k2b.item():.4f} (True: {true_k2b})\")\n", + "print(f\"Inferred k3: {pinn.k3.item():.4f} (True: {true_k3})\")\n", + "print(f\"Inferred t_switch: {pinn.t_switch.item():.4f} (True: {true_t_switch})\")\n", + "\n", + "# --- Visualization ---\n", + "plt.figure(figsize=(18, 10))\n", + "\n", + "# Plot Loss History\n", + "plt.subplot(2, 3, 1)\n", + "plt.plot(history['loss'], label='Total Loss')\n", + "plt.plot(history['data_loss'], label='Data Loss')\n", + "plt.plot(history['physics_loss'], label='Physics Loss (Raw)')\n", + "plt.plot(history['scaled_physics_loss'], label='Physics Loss (Scaled)')\n", + "plt.plot(history['known_points_loss'], label='Known Points Loss (Raw)') # Plot raw known points loss\n", + "plt.plot(history['scaled_known_points_loss'], label='Known Points Loss (Scaled)') # Plot scaled known points loss\n", + "plt.yscale('log')\n", + "plt.title('Loss History')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "plt.legend(fontsize='small')\n", + "plt.grid(True)\n", + "\n", + "# Plot Inferred Spring Constants\n", + "plt.subplot(2, 3, 2)\n", + "plt.plot(history['k1a'], label='Inferred k1 (before)')\n", + "plt.axhline(y=true_k1a, color='r', linestyle='--', label=f'True k1a ({true_k1a})')\n", + "plt.plot(history['k1b'], label='Inferred k1 (after)')\n", + "plt.axhline(y=true_k1b, color='r', linestyle=':', label=f'True k1b ({true_k1b})')\n", + "\n", + "plt.plot(history['k2a'], label='Inferred k2 (before)')\n", + "plt.axhline(y=true_k2a, color='g', linestyle='--', label=f'True k2a ({true_k2a})')\n", + "plt.plot(history['k2b'], color='g', linestyle='-.', label='Inferred k2 (after)') # Changed style for better visibility\n", + "plt.axhline(y=true_k2b, color='g', linestyle=':', label=f'True k2b ({true_k2b})')\n", + "\n", + "plt.plot(history['k3'], label='Inferred k3')\n", + "plt.axhline(y=true_k3, color='b', linestyle='--', label=f'True k3 ({true_k3})')\n", + "\n", + "plt.title('Inferred Spring Constants')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Spring Constant Value')\n", + "plt.legend(fontsize='small')\n", + "plt.grid(True)\n", + "\n", + "# Plot Inferred Switching Time\n", + "plt.subplot(2, 3, 3)\n", + "plt.plot(history['t_switch'], label='Inferred t_switch')\n", + "plt.axhline(y=true_t_switch, color='purple', linestyle='--', label=f'True t_switch ({true_t_switch})')\n", + "plt.title('Inferred Switching Time')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Time (s)')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "\n", + "# Plot Learning Rate\n", + "plt.subplot(2, 3, 4)\n", + "plt.plot(history['current_lr'], label='Learning Rate')\n", + "plt.yscale('log')\n", + "plt.title('Learning Rate Schedule')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Learning Rate')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "\n", + "# Plot Adaptive Lambda Physics\n", + "plt.subplot(2, 3, 5)\n", + "plt.plot(history['adaptive_lambda_physics'], label='Adaptive Physics Loss Weight')\n", + "plt.plot(history['adaptive_lambda_known_points'], label='Adaptive Known Points Loss Weight') # Plot new adaptive lambda\n", + "plt.title('Adaptive Loss Weights')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Lambda Value')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Plot PINN predictions vs. true data\n", + "t_plot_np = np.linspace(time_span[0], time_span[1], 500).reshape(-1, 1)\n", + "t_plot_torch = torch.tensor(t_plot_np, dtype=torch.float32, device=device)\n", + "x_pred_plot_torch = pinn(t_plot_torch).detach().cpu().numpy()\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "plt.plot(t_data.cpu().numpy(), x_data.cpu().numpy()[:, 0], 'r.', alpha=0.6, label='Data $x_1$')\n", + "plt.plot(t_data.cpu().numpy(), x_data.cpu().numpy()[:, 1], 'g.', alpha=0.6, label='Data $x_2$')\n", + "plt.plot(t_data.cpu().numpy(), x_data.cpu().numpy()[:, 2], 'b.', alpha=0.6, label='Data $x_3$')\n", + "\n", + "plt.plot(t_plot_np, x_pred_plot_torch[:, 0], 'r-', linewidth=2, label='PINN $x_1$')\n", + "plt.plot(t_plot_np, x_pred_plot_torch[:, 1], 'g-', linewidth=2, label='PINN $x_2$')\n", + "plt.plot(t_plot_np, x_pred_plot_torch[:, 2], 'b-', linewidth=2, label='PINN $x_3$')\n", + "\n", + "# Plot the known points (initial/boundary conditions)\n", + "# Detach both t_known_points and x_known_true_tensor before converting to numpy\n", + "plt.plot(t_known_points.cpu().detach().numpy(), x_known_true_tensor.cpu().detach().numpy()[:, 0], 'rx', markersize=8, mew=2, label='Known $x_1$ Points')\n", + "plt.plot(t_known_points.cpu().detach().numpy(), x_known_true_tensor.cpu().detach().numpy()[:, 1], 'gx', markersize=8, mew=2, label='Known $x_2$ Points')\n", + "plt.plot(t_known_points.cpu().detach().numpy(), x_known_true_tensor.cpu().detach().numpy()[:, 2], 'bx', markersize=8, mew=2, label='Known $x_3$ Points')\n", + "\n", + "inferred_t_switch = pinn.t_switch.item()\n", + "plt.axvline(x=inferred_t_switch, color='k', linestyle='--', label=f'Inferred Switch Time ({inferred_t_switch:.2f}s)')\n", + "plt.axvline(x=true_t_switch, color='m', linestyle='-.', label=f'True Switch Time ({true_t_switch:.2f}s)')\n", + "\n", + "plt.title('PINN Predictions vs. Synthetic Data and Known Points')\n", + "plt.xlabel('Time (s)')\n", + "plt.ylabel('Displacement')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62c5d47f-9e29-4de4-9ffe-9c5ee2b629a8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}