A multiscale transformer with a new collective-based parallel attention method is claimed to be the first deep-learning model to reproduce small-scale turbulence statistics down to the viscous limit in 3D flow.
Concerning the Use of Turbulent Flow Data for Machine Learning
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abstract
This article describes some common issues encountered in the use of Direct Numerical Simulation (DNS) turbulent flow data for machine learning. We focus on two specific issues; 1) the requirements for a fair validation set, and 2) the pitfalls in downsampling DNS data before training. We attempt to shed light on the impact these issues can have on machine learning and computer vision for turbulence. Further, we include statistical and spectral analysis for the homogenous isotropic turbulence from the John Hopkins Turbulence Database, a Kolmogorov flow, and a Rayleigh-B\'enard Convection Cell using data generated by the authors, to concretely demonstrate these issues.
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physics.flu-dyn 1years
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Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit
A multiscale transformer with a new collective-based parallel attention method is claimed to be the first deep-learning model to reproduce small-scale turbulence statistics down to the viscous limit in 3D flow.