EddyFormer: Accelerated Neural Simulations of Three-Dimensional Turbulence at Scale
Yiheng Du, Aditi Krishnapriyan
Computationally resolving the spatiotemporal evolution of fluid flows is central for many engineering applications. A key challenge arises from turbulence modeling, due to the complex interactions across a wide range of scales, especially in fluid domains with complicated geometries. Fully resolving large-scale turbulence through direct numerical simulation (DNS) is computationally prohibitive, motivating machine learning alternatives. In this work, we propose EddyFormer, a Transformer-based spectral-element model designed for large-scale turbulence simulations. EddyFormer decomposes the prediction into grid-scale and subgrid-scale fields, using attention mechanisms for global dependencies and convolutions for local dynamics. We train EddyFormer on isotropic turbulence, demonstrating that it achieves accuracy of DNS at 256^3 resolution while providing a 30x speedup over DNS, significantly outperforming existing ML baselines in both accuracy and efficiency. Benchmarks on diverse turbulent flows further validate its effectiveness, resolving flow where other models fail and generalizing well to unseen domains.