Gen-PINNs: Generative Adversarial Physics Informed Neural Networks for solving partial differential equations
Gen-PINNs is a new method that uses generative adversarial networks to solve partial differential equations (PDEs). It combines learning from physics constraints with an adversarial training objective and Fourier feature embeddings. This approach fixes problems in standard PINNs by improving accuracy, handling complex features, and stabilizing optimization for difficult equations.