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

• URL: http://arxiv.org/abs/2602.10150v1
• Date: Mon, 09 Feb 2026 18:37:18 GMT
• Title: PEST: Physics-Enhanced Swin Transformer for 3D Turbulence Simulation
• Authors: Yilong Dai, Shengyu Chen, Xiaowei Jia, Peyman Givi, Runlong Yu,
• Abstract summary: Direct numerical simulation (DNS) offers the highest fidelity but is computationally prohibitive.<n>Existing data-driven alternatives struggle with stable long-horizon rollouts, physical consistency, and faithful simulation of small-scale structures.<n>We propose a Physics-Enhanced Swin Transformer (PEST) for 3D turbulence simulation.
• Score: 19.744829080390165
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• i-PhysGaussian: Implicit Physical Simulation for 3D Gaussian Splatting [60.46736489360263]
  i-PhysGaussian is a framework that couples 3D Gaussian Splatting (3DGS) with an implicit Material Point Method (MPM) integrator.<n>Unlike explicit methods, our solution obtains an end-of-step state by minimizing a momentum-balance residual.<n>Results demonstrate that i-PhysGaussian maintains stability at up to 20x larger time steps than explicit baselines.
  arXiv  Detail & Related papers  (2026-02-19T06:38:35Z)
• Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time [57.30651532625017]
  We present a novel hybrid method that integrates numerical simulation, neural physics, and generative control.<n>Our system demonstrates robust performance across diverse 2D/3D scenarios, material types, and obstacle interactions.<n>We promise to release both models and data upon acceptance.
  arXiv  Detail & Related papers  (2025-05-25T01:27:18Z)
• Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark and Learning Strategy [88.8665000676562]
  Prior methods often simplify the problem to low-speed or 2D settings, limiting their applicability to real-world 3D tasks.<n>To mitigate data scarcity, we introduce a novel simulation framework and benchmark grounded in reduced-order dynamics.<n>We propose Dynamics Informed Diffusion Policy (DIDP), a framework that integrates imitation pretraining with physics-informed test-time adaptation.
  arXiv  Detail & Related papers  (2025-05-23T03:28:25Z)
• AMR-Transformer: Enabling Efficient Long-range Interaction for Complex Neural Fluid Simulation [33.63726923336252]
  We propose AMR-Transformer, an efficient and accurate neural CFD-solving pipeline.<n>It integrates a novel adaptive mesh refinement scheme with a Navier-Stokes constraint-aware fast pruning module.<n>Our approach achieves an order-of-magnitude improvement in accuracy over baseline models.
  arXiv  Detail & Related papers  (2025-03-13T11:16:42Z)
• GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
  GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels.<n>We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter.<n>In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
  arXiv  Detail & Related papers  (2024-12-23T18:58:17Z)
• A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport [0.0]
  We introduce the Dual-Stage Temporal Three-dimensional Super-resolution (DST3D-UNet-SR) model for plume dispersion prediction.<n>It is composed of two sequential modules: the temporal module (TM), which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data, and the spatial refinement module (SRM), which subsequently enhances the spatial resolution of the predictions.
  arXiv  Detail & Related papers  (2024-12-14T19:43:48Z)
• Comparison of Generative Learning Methods for Turbulence Modeling [1.2499537119440245]
  High resolution techniques such as Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES) are generally not computationally affordable. Recent advances in machine learning, specifically in generative probabilistic models, offer promising alternatives for turbulence modeling. This paper investigates the application of three generative models - Variational Autoencoders (VAE), Deep Conversaal Generative Adversarial Networks (DCGAN), and Denoising Diffusion Probabilistic Models (DDPM)
  arXiv  Detail & Related papers  (2024-11-25T14:20:53Z)
• Fourier neural operators for spatiotemporal dynamics in two-dimensional turbulence [3.0954913678141627]
  We identify that the Fourier neural operator (FNO) based models combined with a partial differential equation (PDE) solver can accelerate fluid dynamic simulations. We also discuss the pitfalls of purely data-driven approaches that need to be avoided by the machine learning models to become viable and competitive tools for long time simulations of turbulence.
  arXiv  Detail & Related papers  (2024-09-23T02:02:02Z)
• Physics-enhanced Neural Operator for Simulating Turbulent Transport [9.923888452768919]
  This paper presents a physics-enhanced neural operator (PENO) that incorporates physical knowledge of partial differential equations (PDEs) to accurately model flow dynamics. The proposed method is evaluated through its performance on two distinct sets of 3D turbulent flow data.
  arXiv  Detail & Related papers  (2024-05-31T20:05:17Z)
• DeepSimHO: Stable Pose Estimation for Hand-Object Interaction via Physics Simulation [81.11585774044848]
  We present DeepSimHO, a novel deep-learning pipeline that combines forward physics simulation and backward gradient approximation with a neural network. Our method noticeably improves the stability of the estimation and achieves superior efficiency over test-time optimization.
  arXiv  Detail & Related papers  (2023-10-11T05:34:36Z)
• Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
  Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs. We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling. We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2022-06-10T04:54:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.