Construction of reduced-order models for fluid flows using deep feedforward neural networks

@inproceedings{Lui2019ConstructionOR,
  title={Construction of reduced-order models for fluid flows using deep feedforward neural networks},
  author={Hugo Felippe da Silva Lui and William R. Wolf},
  year={2019}
}
We present a numerical methodology for construction of reduced order models, ROMs, of fluid flows through the combination of flow modal decomposition and regression analysis. Spectral proper orthogonal decomposition, SPOD, is applied to reduce the dimensionality of the model and, at the same time, filter the POD temporal modes. The regression step is performed by a deep feedforward neural network, DNN, and the current framework is implemented in a context similar to the sparse identification of… CONTINUE READING
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