Out-of-distribution transfer of PDE foundation models to material dynamics under extreme loading

Mahindra Singh Rautela, Siddharth Mansingh, Aleksandra Pachalieva, Alexander Most, Kyle S. Hickmann, Daniel O'Malley, Alexander Scheinker, Bradley C. Love, Diane Oyen, Nathan A. DeBardeleben, Earl Lawrence, Ayan Biswas

Keywords: Scientific Machine Learning, PDE Foundation Models, Materials Under Extreme Loading

Abstract: Most PDE foundation models are pretrained and fine-tuned on fluid-centric benchmarks. Their utility under extreme-loading material dynamics remains unclear. We benchmark out-of-distribution transfer on two discontinuity-dominated regimes in which shocks, evolving interfaces, and fracture produce highly non-smooth fields: shock-driven multi-material interface dynamics (perturbed layered interface or PLI) and dynamic fracture/failure evolution (FRAC). We formulate the downstream task as terminal-state prediction, i.e., learning a long-horizon map that predicts the final state directly from the first snapshot without intermediate supervision. Using a unified training and evaluation protocol, we evaluate two open-source pretrained PDE foundation models, POSEIDON and MORPH, and compare fine-tuning from pretrained weights against training from scratch across training-set sizes to quantify sample efficiency under distribution shift.

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Submission Number: 144