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

cs.AI

Submitted: 2026-10-02

Updated: 2026-10-05

Comments: Accepted to NeurIPS 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Sources

• Physics-based Deep Learning
• Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learning
• PhysiX: A Foundation Model for Physics Simulations
• GIST: Gauge-Invariant Spectral Transformers for Scalable Graph Neural Operators
• GeoTransolver: Learning Physics on Irregular Domains Using Multi-scale Geometry Aware Physics Attention Transformer
• DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics

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