ROMs

Reduced-order modeling of advection-dominatedsystems with recurrent neural networks andconvolutional autoencoders

A common strategy for the dimensionality reduction of nonlinear partial dif-ferential equations relies on the use of the proper orthogonal decomposition(POD) to identify a reduced subspace and the Galerkin projection for evolv-ing dynamics in this …

Time-series learning of latent-space dynamics for reduced-order model closure

We study the performance of long short-term memory networks (LSTMs) and neural ordinarydifferential equations (NODEs) in learning latent-space representations of dynamical equations for anadvection-dominated problem given by the viscous Burgers …