Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks

Physics-Informed Neural Networks (PINNs) often suffer from slow convergence and instability due to complex loss landscapes. This paper proposes a lightweight, curvature-aware optimization framework that augments existing first-order optimizers to improve convergence speed, training stability, and solution accuracy on partial differential equations (PDEs).