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

• URL: http://arxiv.org/abs/2603.04354v1
• Date: Wed, 04 Mar 2026 18:19:35 GMT
• Title: Out-of-distribution transfer of PDE foundation models to material dynamics under extreme loading
• Authors: Mahindra Rautela, Alexander Most, Siddharth Mansingh, Aleksandra Pachalieva, Bradley Love, Daniel O Malley, Alexander Scheinker, Kyle Hickmann, Diane Oyen, Nathan Debardeleben, Earl Lawrence, Ayan Biswas,
• Abstract summary: Most PDE foundation models are pretrained and fine-tuned on fluid-centric benchmarks.<n>We benchmark out-of-distribution transfer on two discontinuity-dominated regimes in which shocks, evolving interfaces, and fracture produce highly non-smooth fields.<n>We evaluate two open-source 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.
• Score: 86.6550968435969
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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