The limits of exactness: On the failure of automatic differentiation in physics-informed machine learning

Abstract:Automatic differentiation (AD) lets neural networks compute derivatives of governing equations to machine precision, and this precision has made it the computational backbone of physics-informed machine learning. Yet exactness in the mathematical sense is not the same as fidelity to the physics. Here I argue that a derivative can be numerically perfect and still be the wrong derivative for the problem at hand, because AD, by construction, has no notion of the physical structure a solution must obey. Convection and its associated directionality, diffusion, and dispersion are only the most visible instances of a much longer list that spans all branches of computational science and engineering, including conservation, thermodynamic consistency, symmetry, symplectic structure, positivity, monotonicity, and boundedness. Recognizing this broader gap reframes how the field should build the next generation of PDE-driven neural surrogates.

Machine Learning (cs.LG)

arXiv:2609.33078 [cs.LG]

(or arXiv:2609.33078v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.33078 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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