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.
Small scales, many species and the manifold challenges of turbulent combustion.Proceedings of the Combustion Institute, 34(1):1–31
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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.