Work

Data‐Driven Learning of Reduced‐Order Dynamics for a Parametrized Shallow Water Equation

Year: 2021

Type: article

Abstract: A non‐intrusive data‐driven model order reduction method is introduced that learns low‐dimensional dynamical models for a parametrized non‐traditional shallow water equation (NTSWE) [1]. The reduced‐order model is learnt by setting an appropriate least‐squares optimization problem [2] in a low‐dimensional subspace. The non‐intrusive model order reduction framework is extended to a parametric case using the parameter dependency at the level of the partial differential equation. The efficiency of the proposed non‐intrusive method is illustrated to construct reduced‐order models for NTSWE and compared with an intrusive method, proper orthogonal decomposition with Galerkin projection. Furthermore the predictability of both models outside the range of the training data is discussed.

Source: PAMM

DOI: 10.1002/pamm.202000360

Authors: Süleyman Yıldız, Pawan Goyal, Peter Benner, Bülent Karasözen

Institutions: Middle East Technical University, Max Planck Institute for Dynamics of Complex Technical Systems

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg

OpenAlex ID: W3045690184

FWCI: 0.512

Cites: 37

Cited by: 8

Related to: 10