From Zero to Turbulence: Generative Modeling for 3D Flow Simulation

• URL: http://arxiv.org/abs/2306.01776v3
• Date: Thu, 14 Mar 2024 22:46:25 GMT
• Title: From Zero to Turbulence: Generative Modeling for 3D Flow Simulation
• Authors: Marten Lienen, David Lüdke, Jan Hansen-Palmus, Stephan Günnemann,
• Abstract summary: We propose to approach turbulent flow simulation as a generative task directly learning the manifold of all possible turbulent flow states without relying on any initial flow state. Our generative model captures the distribution of turbulent flows caused by unseen objects and generates high-quality, realistic samples for downstream applications.
• Score: 45.626346087828765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simulations of turbulent flows in 3D are one of the most expensive simulations in computational fluid dynamics (CFD). Many works have been written on surrogate models to replace numerical solvers for fluid flows with faster, learned, autoregressive models. However, the intricacies of turbulence in three dimensions necessitate training these models with very small time steps, while generating realistic flow states requires either long roll-outs with many steps and significant error accumulation or starting from a known, realistic flow state - something we aimed to avoid in the first place. Instead, we propose to approach turbulent flow simulation as a generative task directly learning the manifold of all possible turbulent flow states without relying on any initial flow state. For our experiments, we introduce a challenging 3D turbulence dataset of high-resolution flows and detailed vortex structures caused by various objects and derive two novel sample evaluation metrics for turbulent flows. On this dataset, we show that our generative model captures the distribution of turbulent flows caused by unseen objects and generates high-quality, realistic samples amenable for downstream applications without access to any initial state.

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