# Backward Reconstruction of the Chafee--Infante Equation via Physics-Informed WGAN-GP

arXiv: https://arxiv.org/abs/2601.07733 · HTML: https://mlsanity.com/paper/2601.07733/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Builds on (most cited first, 2 of 2)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)

## Abstract (arXiv)

We present a physics-informed Wasserstein GAN with gradient penalty (WGAN-GP) for solving the inverse Chafee--Infante problem on two-dimensional domains with Dirichlet boundary conditions. The objective is to reconstruct an unknown initial condition from a near-equilibrium state obtained after 100 explicit forward Euler iterations of the reaction-diffusion equation [ ut - γDelta u + kappaleft(u3 - uright)=0. ] Because this mapping strongly damps high-frequency content, the inverse problem is severely ill-posed and sensitive to noise. Our approach integrates a U-Net generator, a PatchGAN critic with spectral normalization, Wasserstein loss with gradient penalty, and several physics-informed auxiliary terms, including Lyapunov energy matching, distributional statistics, and a crucial forward-simulation penalty. This penalty enforces consistency between the predicted initial condition and its forward evolution under the same forward Euler discretization used for dataset generation. Earlier experiments employing an Eyre-type semi-implicit solver were not compatible with this residual mechanism due to the cost and instability of Newton iterations within batched GPU training. On a dataset of 50k training and 10k testing pairs on 128×128 grids (with natural [-1,1] amplitude scaling), the best trained model attains a mean absolute error (MAE) of approximately 0.23988159 on the full test set, with a sample-wise standard deviation of about 0.00266345. The results demonstrate stable inversion, accurate recovery of interfacial structure, and robustness to high-frequency noise in the initial data.