PEST: Physics-Enhanced Swin Transformer for 3D Turbulence Simulation

TL;DR

PEST is a novel physics-enhanced transformer model that improves 3D turbulence simulation by capturing small-scale structures, ensuring physical consistency, and enabling stable long-term predictions.

Contribution

It introduces a window-based self-attention mechanism and a frequency-domain adaptive loss, integrating physical constraints for more accurate turbulence modeling.

Findings

PEST outperforms existing data-driven methods in accuracy and stability.

It effectively captures small-scale turbulent structures.

The model maintains physical consistency over long simulations.

Abstract

Accurate simulation of turbulent flows is fundamental to scientific and engineering applications. Direct numerical simulation (DNS) offers the highest fidelity but is computationally prohibitive, while existing data-driven alternatives struggle with stable long-horizon rollouts, physical consistency, and faithful simulation of small-scale structures. These challenges are particularly acute in three-dimensional (3D) settings, where the cubic growth of spatial degrees of freedom dramatically amplifies computational cost, memory demand, and the difficulty of capturing multi-scale interactions. To address these challenges, we propose a Physics-Enhanced Swin Transformer (PEST) for 3D turbulence simulation. PEST leverages a window-based self-attention mechanism to effectively model localized PDE interactions while maintaining computational efficiency. We introduce a frequency-domain adaptive loss that explicitly emphasizes small-scale structures, enabling more faithful simulation of high-frequency dynamics. To improve physical consistency, we incorporate Navier--Stokes residual constraints and divergence-free regularization directly into the learning objective. Extensive experiments on two representative turbulent flow configurations demonstrate that PEST achieves accurate, physically consistent, and stable autoregressive long-term simulations, outperforming existing data-driven baselines.