MSPT: Efficient Large-Scale Physical Modeling via Parallelized Multi-Scale Attention
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908872847917056 |
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| author | Curvo, Pedro M. P. van de Meent, Jan-Willem Zhdanov, Maksim |
| author_facet | Curvo, Pedro M. P. van de Meent, Jan-Willem Zhdanov, Maksim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01738 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MSPT: Efficient Large-Scale Physical Modeling via Parallelized Multi-Scale Attention Curvo, Pedro M. P. van de Meent, Jan-Willem Zhdanov, Maksim Machine Learning 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. |
| title | MSPT: Efficient Large-Scale Physical Modeling via Parallelized Multi-Scale Attention |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.01738 |