Deep Learning of Turbulent Scalar Mixing

2018/11/17 by Raissi, Maziar, Babaee, Hessam, Givi, Peyman · 2 citations
#Computational Engineering #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Fluid Dynamics (physics.flu-dyn) #and Science (cs.CE)

paper · doi:10.48550/arxiv.1811.07095

Abstract

Based on recent developments in physics-informed deep learning and deep hidden physics models, we put forth a framework for discovering turbulence models from scattered and potentially noisy spatio-temporal measurements of the probability density function (PDF). The models are for the conditional expected diffusion and the conditional expected dissipation of a Fickian scalar described by its transported single-point PDF equation. The discovered model are appraised against exact solution derived by the amplitude mapping closure (AMC)/ Johnsohn-Edgeworth translation (JET) model of binary scalar mixing in homogeneous turbulence.

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• A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning
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