Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows

Unsteady fluid systems are nonlinear high-dimensional dynamical systems that may exhibit multiple complex phenomena both in time and space. Reduced Order Modeling (ROM) of fluid flows has been an active research topic in the recent decade with the primary goal to decompose complex flows to a set of features most important for future state prediction and control, typically using a dimensionality reduction technique... (read more)

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METHOD TYPE
Memory Network
Working Memory Models
AutoEncoder
Generative Models