Neural-ISAM hybridizes in-situ adaptive tabulation with on-the-fly neural network training to prune manifold databases, lowering memory for complex combustion models in LES of Sandia flames.
High order conservative finite difference scheme for variable density low mach number turbulent flows.Journal of Computational Physics, 227(15):7125–7159
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Neural-ISAM: A hybrid in-situ machine learning approach for complex manifold-based combustion models in LES of turbulent flames
Neural-ISAM hybridizes in-situ adaptive tabulation with on-the-fly neural network training to prune manifold databases, lowering memory for complex combustion models in LES of Sandia flames.