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cs.LG2026
Parameter misspecification in physics-informed neural networks: A closed-form audit and its limits
David McShannon, Nicholas Dietrich
Physics-informed neural networks (PINNs) are trained to minimize the residual of a prescribed equation, so a low training loss does not indicate whether a coefficient in that equat…
cs.LG2026
Silent Failures in Physics-Informed Neural Networks: Parameter Poisoning and the Limits of Loss-Based Validation
David McShannon, Nicholas Dietrich
Physics-informed neural networks (PINNs) embed governing equations in their loss function, enabling mesh-free solutions to partial differential equations. Low training loss is trea…