Transolver: A Transformer Solver for Large-Scale Industrial Geometries

License: arXiv preprint (copyright held by authors) · Updated 2026-02-22

Standard attention over millions of mesh points is prohibitively expensive. Transolver instead learns a slice-based projection that groups mesh points into a modest number of learnable physical states, and performs attention in that compressed space.

Reported gains: State-of-the-art results across standard PDE benchmarks and industrial car/aerodynamics cases, with substantially lower memory than plain Transformers.

Tags: TransformerGeometryIndustrialPDEAttention
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