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derivative-fidelity failure mode

topic1 events
papersTODAY 04:00 UTC

Study identifies derivative-fidelity failure mode in physics-informed neural networks

A new arXiv paper argues that physics-informed neural networks can match target function values while still producing inaccurate derivatives. The authors describe this as a distinct failure mode and provide strengthened benchmark evidence for it, based on models trained only on function values. The work suggests that evaluating PINNs by function agreement alone can mask errors in the derivative terms central to solving differential equations.

pinns/benchmarks/research/autodiff