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Model order reduction assisted by deep neural networks (ROM-net)
2020
Advanced Modeling and Simulation in Engineering Sciences
In this paper, we propose a general framework for projection-based model order reduction assisted by deep neural networks. The proposed methodology, called ROM-net, consists in using deep learning techniques to adapt the reduced-order model to a stochastic input tensor whose nonparametrized variabilities strongly influence the quantities of interest for a given physics problem. In particular, we introduce the concept of dictionary-based ROM-nets, where deep neural networks recommend a suitable
doi:10.1186/s40323-020-00153-6
fatcat:dvqk757qmzcybcyssaptu7s7tu