[2603.04354] Out-of-distribution transfer of PDE foundation models to material dynamics under extreme loading

Computer Science > Machine Learning arXiv:2603.04354 (cs) [Submitted on 4 Mar 2026] 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 View a PDF of the paper titled Out-of-distribution transfer of PDE foundation models to material dynamics under extreme loading, by Mahindra Rautela and 11 other authors View PDF HTML (experimental) 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 qua...