Overcoming Challenges in Physics-Informed Neural Networks (PINNs): Gradient Optimization for Inverse Problems
Physics-Informed Neural Networks (PINNs) represent a promising approach for solving complex Partial Differential Equations (PDEs) and inverse problems, such as determining hidden parameters of a physical system – for instance, viscosity (nu) in a 2D Burgers equation. However, the application of PINNs presents inherent challenges. A recent study by Wang et al. (2020) delves into these limitations and proposes innovative solutions that can significantly enhance PINN performance.