将PDE基础模型迁移到极端加载材料动态，验证其在非光滑场中的泛化能力。
Out-of-distribution transfer of PDE foundation models to material dynamics under extreme loading
- 以终态预测任务统一评估模型在冲击与断裂场景下的迁移性能。
- 在小样本条件下，预训练模型比从零训练更高效，尤其在分布外数据上表现更好。
- 适用于高应力材料模拟、多相流与断裂预测等工程场景研究者。
多数偏微分方程（PDE）基础模型在以流体为主的基准上进行预训练和微调，但在极端加载下的材料动力学中是否有效仍不明确。本文在两个具有强间断特性的非光滑场场景中评估分布外迁移性能：冲击驱动的多材料界面动力学（扰动层状界面，PLI）和动态裂纹/失效演化（FRAC）。将下游任务定义为终态预测，即学习一个长时序映射，直接从初始快照预测最终状态，无需中间监督。采用统一训练与评估协议，测试两个开源预训练模型POSEIDON和MORPH，比较从预训练权重微调与从零训练在不同训练集规模下的表现，量化分布偏移下的样本效率。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。