FluidFormer: Transformer with Continuous Convolution for Particle-based Fluid Simulation

• URL: http://arxiv.org/abs/2508.01537v1
• Date: Sun, 03 Aug 2025 01:44:17 GMT
• Title: FluidFormer: Transformer with Continuous Convolution for Particle-based Fluid Simulation
• Authors: Nianyi Wang, Yu Chen, Shuai Zheng,
• Abstract summary: Learning-based fluid simulation networks have been proven as viable alternatives to traditional numerical solvers for the Navier-Stokes equations.<n>We propose the first Fluid Attention Block (FAB) with a local-global hierarchy, where continuous convolutions extract local features while self-attention captures global dependencies.<n>We pioneer the first Transformer architecture specifically designed for continuous fluid simulation, seamlessly integrated within a dual-pipeline architecture.
• Score: 5.167355296859346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning-based fluid simulation networks have been proven as viable alternatives to traditional numerical solvers for the Navier-Stokes equations. Existing neural methods follow Smoothed Particle Hydrodynamics (SPH) frameworks, which inherently rely only on local inter-particle interactions. However, we emphasize that global context integration is also essential for learning-based methods to stabilize complex fluid simulations. We propose the first Fluid Attention Block (FAB) with a local-global hierarchy, where continuous convolutions extract local features while self-attention captures global dependencies. This fusion suppresses the error accumulation and models long-range physical phenomena. Furthermore, we pioneer the first Transformer architecture specifically designed for continuous fluid simulation, seamlessly integrated within a dual-pipeline architecture. Our method establishes a new paradigm for neural fluid simulation by unifying convolution-based local features with attention-based global context modeling. FluidFormer demonstrates state-of-the-art performance, with stronger stability in complex fluid scenarios.

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