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arXiv · 2512.01738

MSPT: Efficient Large-Scale Physical Modeling via Parallelized Multi-Scale Attention

Pedro M. P. Curvo·Jan-Willem van de Meent·Maksim Zhdanov
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Abstract

A key scalability challenge in neural solvers for industrial-scale physics simulations is efficiently capturing both fine-grained local interactions and long-range global dependencies across millions of spatial elements. We introduce the Multi-Scale Patch Transformer (MSPT), an architecture that combines local point attention within patches with global attention to coarse patch-level representations. To partition the input domain into spatially-coherent patches, we employ ball trees, which handle irregular geometries efficiently. This dual-scale design enables MSPT to scale to millions of points on a single GPU. We validate our method on standard PDE benchmarks (elasticity, plasticity, fluid dynamics, porous flow) and large-scale aerodynamic datasets (ShapeNet-Car, Ahmed-ML), achieving state-of-the-art accuracy with substantially lower memory footprint and computational cost.

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Pedro M. P. Curvo, Jan-Willem van de Meent, Maksim Zhdanov. 2025-12-01. MSPT: Efficient Large-Scale Physical Modeling via Parallelized Multi-Scale Attention. https://arxiv.org/abs/2512.01738

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