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

arXiv:2610.03363v3 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. Conseque