Geometry Meets Physics: Data-Efficient Pre-Training for Unstructured Neural PDE Solvers

Excerpt

arXiv:2610.03363v2 Announce Type: replace Abstract: Neural surrogate models for Partial Differential Equations (PDEs) on unstructured 3D geometries are often limited by poor generalization and the high cost of generating large-scale training datasets. Consequently, pre-training on massive datasets of related PDE dynamics has emerged as a critical alternative to enhance the robustness and scalability of these models. However, this strategy is neither compute- nor data-efficient, as it relies on m