Item Infomation
Title: |
Fast data-driven model reduction for nonlinear dynamical systems |
Authors: |
Joar, Axås Mattia, Cenedese George, Haller |
Issue Date: |
2022 |
Publisher: |
Springer |
Abstract: |
We present a fast method for nonlinear data-driven model reduction of dynamical systems onto their slowest nonresonant spectral submanifolds (SSMs). While the recently proposed reduced-order modeling method SSMLearn uses implicit optimization to fit a spectral submanifold to data and reduce the dynamics to a normal form, here, we reformulate these tasks as explicit problems under certain simplifying assumptions. In addition, we provide a novel method for timelag selection when delay-embedding signals from multimodal systems. We show that our alternative approach to data-driven SSM construction yields accurate and sparse rigorous models for essentially nonlinear (or non-linearizable) dynamics on both numerical and experimental datasets. |
Description: |
CC BY |
URI: |
https://link.springer.com/article/10.1007/s11071-022-08014-0 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7966 |
Appears in Collections |
OER - Kỹ thuật điện; Điện tử - Viễn thông |
ABSTRACTS VIEWS
25
FULLTEXT VIEWS
114
Files in This Item: