Exact Enforcement of Temporal Continuity in Sequential Physics-Informed Neural Networks

arXiv:2403.03223v1 Announce Type: new
Abstract: The use of deep learning methods in scientific computing represents a potential paradigm shift in engineering problem solving. One of the most prominent developments is Physics-Informed Neural Networks (PINNs), in which neural networks are trained to satisfy partial differential equations (PDEs) and/or observed data. While this method shows promise, the standard version has been shown to struggle in accurately predicting the dynamic behavior of time-dependent problems. To address this challenge, methods have been proposed that …

abstract arxiv computing continuity cs.lg data deep learning differential engineering networks neural networks paradigm physics physics.comp-ph physics-informed shift temporal type