Scaling 3D Physics Surrogates: Low-Fidelity Pretraining for High-Fidelity Transfer

Training neural surrogates for high-fidelity simulation requires expensive labels, limiting the scale of available supervision. We investigate low-fidelity simulation as a reusable source of pretraining data for unstructured 3D physics. Our approach is simple yet effective: using open-source fluid and solid-mechanics solvers, we generate approximate responses on source geometries constructed independently of downstream datasets, pretrain standard surrogate backbones, and finetune them on high-fidelity targets. Across unstructured downstream tasks spanning fluid fields, deformable-body responses, and physical systems whose governing equations are absent from pretraining, low-fidelity pretraining improves over training from scratch and competing pretraining baselines. Our pretraining can reduce high-fidelity data requirements by up to 77% at matched accuracy. Transfer remains effective when pretraining uses a single fixed physical condition and varies only geometry, demonstrating that this restricted source distribution already provides useful supervision for downstream adaptation.We further study scaling with pretraining-corpus size, model capacity, and physical-condition coverage, observing task-dependent improvements in downstream transfer. These results demonstrate low-fidelity simulation as an effective source of large-scale, reusable supervision for high-fidelity surrogates on unstructured 3D geometry.