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Efficient bifurcation and parameterization of multi-dimensional combustion manifolds using deep mixture of experts: an a priori study
[article]
2019
arXiv
pre-print
This work describes and validates an approach for autonomously bifurcating turbulent combustion manifolds to divide regression tasks amongst specialized artificial neural networks (ANNs). This approach relies on the mixture of experts (MoE) framework, where each neural network is trained to be specialized in a given portion of the input space. The assignment of different input regions to the experts is determined by a gating network, which is a neural network classifier. In some previous
arXiv:1910.10765v2
fatcat:uqrk5yo3jncyvhryzhiazfsshy