Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs

Kentaro Hoshisashi, Carolyn E Phelan, Paolo Barucca

Physics-Informed Neural Networks (PINNs) recast PDE solving as an optimisation problem in function space by minimising a residual-based objective, yet many applications require add…