Physics Informed Neural Networks for damped harmonic oscillator and Burger's Equation (with extrapolation analysis) [P]

I built a PINN implementation in Python to solve two problems as part of a physics exam project: the damped harmonic oscillator (2nd-order ODE) and the 1D viscid Burgers' equation (nonlinear PDE). Both forward and inverse problems (to estimate unknown equation parameters from data) are implemented for each problem.

The repo includes source code, sample outputs, and the written exam report (PDF). Beyond the standard PINN training setup, I ran a comparison against non-physics-informed baselines and specifically investigated extrapolation behavior, i.e. how well the models generalize outside the training domain, and finally made statistical analyses of the parameter estimation performance.

GitHub: https://github.com/desdb6/pinn-dho-burgers

Ready-to-run demo scripts are included, and the modules are structured to be importable so you can write your own training scripts for more customization. This is not novel research, just a clean student implementation, but hopefully useful to others learning about PINNs. Happy to answer questions or receive feedback in the comments.