A derivative-fidelity failure mode in physics-informed neural networks: strengthened benchmark evidence from function-value training

Physics-informed neural networks (PINNs) use automatic differentiation to impose differential-equation residuals, but good agreement in function values does not necessarily imply accurate derivatives. This paper formulates derivative fidelity as a failure mode of PINNs and tests it with one-dimensio