From Deep to Physics-Informed Learning of Turbulence: Diagnostics

16 Oct 2018Ryan KingOliver HennighArvind MohanMichael Chertkov

We describe tests validating progress made toward acceleration and automation of hydrodynamic codes in the regime of developed turbulence by three Deep Learning (DL) Neural Network (NN) schemes trained on Direct Numerical Simulations of turbulence. Even the bare DL solutions, which do not take into account any physics of turbulence explicitly, are impressively good overall when it comes to qualitative description of important features of turbulence... (read more)

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