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