Abstract
The SGS mode] in LES is estimated by neural network, which is one of the methods in machine learning. Training data for learning are obtained by direct numerical simulation of turbulent channel flow with various Reynolds numbers. We use velocity gradient tensors and distance from the wall as inputs aiming at improving conventional SGS model. High correlation coefficients are obtained between SGS stress tensors by DNS and those by neural network model.
