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We present two families of sub-grid scale (SGS) turbulence models developedfor large-eddy simulation (LES) purposes. Their development required theformulation of physics-informed robust and efficient Deep Learning (DL)algorithms which, unlike state-of-the-art analytical modeling techniques canproduce high-order complex non-linear relations between inputs and outputs.Explicit filtering of data from direct simulations of the canonical channelflow at two friction Reynolds numbers $Re_\tau\approx 395$ and 590 providedaccurate data for training and testing. The two sets of models use differentnetwork architectures. One of the architectures uses tensor basis neuralnetworks (TBNN) and embeds the simplified analytical model form of the generaleffective-viscosity hypothesis, thus incorporating the Galilean, rotational andreflectional invariances. The other architecture is that of a relatively simplenetwork, that is able to incorporate the Galilean invariance only. However,this simpler architecture has better feature extraction capacity owing to itsability to establish relations between and extract information fromcross-components of the integrity basis tensors and the SGS stresses. Both setsof models are used to predict the SGS stresses for feature datasets generatedwith different filter widths, and at different Reynolds numbers. It is shownthat due to the simpler model's better feature learning capabilities, itoutperforms the invariance embedded model in statistical performance metrics.In a priori tests, both sets of models provide similar levels of dissipationand backscatter. Based on the test results, both sets of models should beusable in a posteriori actual LESs.

Accurate deep learning sub-grid scale models for large eddy simulations