Do Physics Foundation Models Learn Generalizable Physics? A Bias-Aware Benchmark Across Physical Regimes and Distribution Shifts
arXiv:2605.29283v1 Announce Type: new Abstract: Recent physics foundation models claim general spatiotemporal forecasting ability, yet their evaluations often collapse performance into a single average score under a fixed training distribution. This makes it difficult to determine whether a model has learned generalizable physical dynamics or only performs well under particular settings. We construct a benchmark with 8 physical dynamics, 3 training-data mixtures, and 25 test regimes induced by dynamic-scale and initial-condition complexity shifts, covering in-distribution, distribution-shift,
The proliferation of 'Physics Foundation Models' and their claims of generalizable forecasting abilities necessitates rigorous, bias-aware evaluation to validate their practical utility.
This benchmark provides critical methodology for assessing the true generalizability of AI models in scientific domains, directly impacting their adoption and reliability in physics and engineering applications.
The focus shifts from raw performance metrics to an emphasis on robustness across diverse physical regimes and distribution shifts, demanding more resilient and truly intelligent AI systems.
- · Researchers developing robust and generalizable AI models
- · Industries relying on accurate physical simulations
- · Academic institutions focused on AI ethics and testing
- · AI models with narrow applicability
- · Claims of 'general intelligence' without empirical backing
- · Organizations relying on unverified AI model performance
Improved understanding of the current limitations and capabilities of physics foundation models.
Accelerated development of more generalizable and trustworthy AI for scientific discovery and engineering design.
New standards for AI model evaluation become industry norms, raising the bar for AI deployment in critical sectors.
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Read at arXiv cs.LG