AI RESEARCH
Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs
arXiv CS.LG
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ArXi:2604.13723v1 Announce Type: new 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 additional derivative-based relations that are just as fundamental as the governing equations. In this paper, we present Derivative-Constrained PINNs (DC-PINNs), a general framework that treats constrained PDE solving as an optimisation guided by a minimum objective function criterion where the physics resides in the minimum principle.