Parametric nonlinear model reduction using machine learning on Grassmann manifold with an application on a flow simulation

DOI10.1007/s00332-024-10039-1zbMath1545.6552MaRDI QIDQ6536761

Norapon Sukuntee, Saifon Chaturantabut

Publication date: 13 May 2024

Published in: Journal of Nonlinear Science (Search for Journal in Brave)

proper orthogonal decomposition Grassmann manifold machine learning model order reduction discrete empirical interpolation method

Mathematics Subject Classification ID

Spectral, collocation and related methods for boundary value problems involving PDEs (65N35) Complexity and performance of numerical algorithms (65Y20) Numerical methods for low-rank matrix approximation; matrix compression (65F55)

This page was built for publication: Parametric nonlinear model reduction using machine learning on Grassmann manifold with an application on a flow simulation