Thông tin tài liệu
Nhan đề : | Fast data-driven model reduction for nonlinear dynamical systems |
Tác giả : | Joar, Axås Mattia, Cenedese George, Haller |
Năm xuất bản : | 2022 |
Nhà xuất bản : | Springer |
Tóm tắt : | 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. |
Mô tả: | CC BY |
URI: | https://link.springer.com/article/10.1007/s11071-022-08014-0 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7966 |
Bộ sưu tập | OER - Kỹ thuật điện; Điện tử - Viễn thông |
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