{"ID":2848974,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.24173","arxiv_id":"2510.24173","title":"EddyFormer: Accelerated Neural Simulations of Three-Dimensional Turbulence at Scale","abstract":"Computationally resolving turbulence remains a central challenge in fluid dynamics due to its multi-scale interactions. Fully resolving large-scale turbulence through direct numerical simulation (DNS) is computationally prohibitive, motivating data-driven machine learning alternatives. In this work, we propose EddyFormer, a Transformer-based spectral-element (SEM) architecture for large-scale turbulence simulation that combines the accuracy of spectral methods with the scalability of the attention mechanism. We introduce an SEM tokenization that decomposes the flow into grid-scale and subgrid-scale components, enabling capture of both local and global features. We create a new three-dimensional isotropic turbulence dataset and train EddyFormer to achieves DNS-level accuracy at 256^3 resolution, providing a 30x speedup over DNS. When applied to unseen domains up to 4x larger than in training, EddyFormer preserves accuracy on physics-invariant metrics-energy spectra, correlation functions, and structure functions-showing domain generalization. On The Well benchmark suite of diverse turbulent flows, EddyFormer resolves cases where prior ML models fail to converge, accurately reproducing complex dynamics across a wide range of physical conditions.","short_abstract":"Computationally resolving turbulence remains a central challenge in fluid dynamics due to its multi-scale interactions. Fully resolving large-scale turbulence through direct numerical simulation (DNS) is computationally prohibitive, motivating data-driven machine learning alternatives. In this work, we propose EddyForm...","url_abs":"https://arxiv.org/abs/2510.24173","url_pdf":"https://arxiv.org/pdf/2510.24173v1","authors":"[\"Yiheng Du\",\"Aditi S. Krishnapriyan\"]","published":"2025-10-28T08:27:37Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"math.DS\",\"math.NA\",\"physics.flu-dyn\"]","methods":"[\"Transformer\"]","has_code":false}