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pinn-training
Train Physics-Informed Neural Networks (PINNs) using DeepXDE. Solve forward and inverse PDE problems by embedding physics equations into the neural network loss function. Supports 1D/2D/3D, time-dependent, and parametric PDEs.
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# PINN Training (DeepXDE)
## Overview
Solve PDEs using Physics-Informed Neural Networks. A neural network approximates the solution u(x,t), trained by minimizing the PDE residual + boundary/initial condition losses. Works for forward problems (solve PDE) and inverse problems (discover parameters from data).
## When to Use
- Complex geometries where meshing is difficult
- Inverse problems (discovering PDE parameters from data)
- Noisy or sparse data with known governing equations
- Multi-physics problems with coupled PDEs
- When you need a differentiable surrogate of the PDE solution
