most citedUsing recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models

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physics.comp-ph2020

Non-autoregressive time-series methods for stable parametric reduced-order models

Romit Maulik, Bethany Lusch, Prasanna Balaprakash

Advection-dominated dynamical systems, characterized by partial differential equations, are found in applications ranging from weather forecasting to engineering design where accur…

physics.comp-ph2019

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

Romit Maulik, Arvind Mohan, Bethany Lusch +3

We study the performance of long short-term memory networks (LSTMs) and neural ordinary differential equations (NODEs) in learning latent-space representations of dynamical equatio…