Decision-Aware Evaluation of Physics-Informed Surrogates

Abstract:Physics-informed machine learning is often assessed by curve error, although engineering use depends on downstream decisions: ranking candidates, avoiding infeasible designs and limiting regret. We introduce pinn-gym, an open benchmark for material-conditioned lattice design that couples a transparent reduced-order crush-and-impact oracle with five printable polymer cards, dimensionless force-response targets and a protocol spanning curve fidelity, physical admissibility, top-k retrieval and mass regret. Across per-material, pooled and cross-material settings, low nRMSE is frequently insufficient to identify useful design selections. Physics-informed losses alter trade-offs rather than monotonically improving all metrics, and dimensionless conditioning improves comparability without making transfer symmetric. The benchmark is not a certified material model; within the released oracle, candidate generator and material cards, pinn-gym provides a reproducible testbed for evaluating PIML surrogates as decision systems rather than curve predictors alone.

12 pages, 5 figures, 9 tables. Code and data available at this https URL

Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE)

I.2.6; I.2.8; J.2

arXiv:2606.07146 [cs.LG]

(or arXiv:2606.07146v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.07146 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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