Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models

no code implementations • 25 Jan 2022 • Federico Fatone, Stefania Fresca, Andrea Manzoni

Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional ROMs - built, e. g., exclusively through proper orthogonal decomposition (POD) - when applied to nonlinear time-dependent parametrized PDEs.

Dimensionality Reduction

Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based ROMs

no code implementations • NeurIPS Workshop DLDE 2021 • Stefania Fresca, Federico Fatone, Andrea Manzoni

Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional ROMs - built, e. g., through proper orthogonal decomposition (POD) - when applied to nonlinear time-dependent parametrized PDEs.

Dimensionality Reduction