Architectural Strategies for the optimization of Physics-Informed Neural
Networks
Architectural Strategies for the optimization of Physics-Informed Neural
Networks
Physics-informed neural networks (PINNs) offer a promising avenue for tackling both forward and inverse problems in partial differential equations (PDEs) by incorporating deep learning with fundamental physics principles. Despite their remarkable empirical success, PINNs have garnered a reputation for their notorious training challenges across a spectrum of PDEs. In this …