Deep Learning of Turbulent Scalar Mixing

Maziar Raissi; Hessam Babaee; Peyman Givi

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arXiv · 1811.07095

Deep Learning of Turbulent Scalar Mixing

Maziar Raissi·Hessam Babaee·Peyman Givi

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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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BibTeX RIS

Maziar Raissi, Hessam Babaee, Peyman Givi. 2018-11-17. Deep Learning of Turbulent Scalar Mixing. https://arxiv.org/abs/1811.07095

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