Physics-informed neural networks (PINNs) as intelligent computing technique for solving partial differential equations: Limitation and future prospects

In recent years, physics-informed neural networks (PINNs) have become a representative method for solving partial differential equations (PDEs) with neural networks. PINNs provide a novel approach to solving PDEs through optimization algorithms, offering a unified framework for solving both forward and inverse problems. However, some limitations in terms of solution accuracy and generality have also been revealed. This paper systematically summarizes the limitations of PINNs and identifies three root causes for their failure in solving PDEs: (1) poor multiscale approximation ability and ill-conditioning caused by PDE losses; (2) insufficient exploration of convergence and error analysis, resulting in weak mathematical rigor; (3) inadequate integration of physical information, causing mismatch between residuals and iteration errors. By focusing on addressing these limitations in PINNs, we outline the future directions and prospects for the intelligent computing of PDEs: (1) analysis of ill-conditioning in PINNs and mitigation strategies; (2) improvements to PINNs by enforcing temporal causality; (3) empowering PINNs with classical numerical methods.