This repository is dedicated to the implementation and exploration of Physics-Informed Neural Networks (PINNs) using DeepXDE and TensorFlow frameworks. PINNs are a novel approach to solving differential equations that are informed by the laws of physics, making them particularly useful for scientific computing applications. This repository contains examples and experiments on various equations, showcasing the power and versatility of PINNs in modeling complex physical systems.
- Equation Solvers: Implementations for solving 1D and 2D Allen-Cahn equations, Heat equations, and inverse problems using PINNs.
- Visualization: Scripts for generating plots and videos to visualize the training process and results.
- Data Management: Utilities for handling input and output data, including temperature datasets and training/test splits.
- Logging: Detailed logging of training sessions for performance monitoring and debugging.