FluidFormer: Transformer with continuous convolution for particle-based fluid simulation

Learning-based fluid simulation has emerged as an efficient alternative to traditional Navier-Stokes solvers. However, existing neural methods that build upon Smoothed Particle Hydrodynamics (SPH) predominantly rely on local particle interactions, which induces instability in complex scenarios due to error accumulation. To address this, we introduce FluidFormer, a novel architecture that establishes a hierarchical local-global modeling paradigm. The core of our model is the Fluid Attention Block (FAB), a co-design that orchestrates continuous convolution for locality with self-attention for global corrective long-range hydrodynamic phenomena. Embedded in a dual-pipeline network, our approach seamlessly fuses inductive physical biases with structured global reasoning. Extensive experiments show that FluidFormer achieves state-of-the-art performance, with significantly improved stability and generalization in challenging fluid scenes, demonstrating its potential as a robust simulator for complex physical systems.