Do Better Scores Mean Better Physics? Physics-Grounded Explanations for Sim2Real Neural Operators
Read the original on arXiv Machine Learning →The paper investigates whether lower prediction error in machine-learning surrogates for fluid dynamics necessarily implies better physical fidelity. Using paired CFD simulations and experimental PIV data for flow around a NACA4418 airfoil, the authors apply a mean‑preserving input intervention that removes velocity fluctuations from selected flow regions. They find that removing fluctuations from the most energetic 10% of cells alters forecasts more than random removal, indicating sensitivity to fluctuation energy. Additionally, a convolutional neural operator (CNO) shows lower overall velocity‑field error but higher two‑component fluctuation‑energy error compared to a reference, and an output attenuation stress test reveals discrepancies between benchmark errors and domain‑summed fluctuation energy. These results suggest that aggregate prediction scores alone are insufficient to assess physical correctness, motivating the inclusion of complementary physical diagnostics.
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