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.
In-situ adaptive manifolds: Enabling computationally efficient simulations of complex turbulent reacting flows.Proceedings of the Combustion Institute, 38(2):2673– 2680
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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.