Data-driven modeling and prediction of non-linearizable dynamics via spectral submanifolds

We develop a methodology to construct low-dimensional predictive models from data sets representing essentially nonlinear (or non-linearizable) dynamical systems with a hyperbolic linear part that are subject to external forcing with finitely many frequencies. Our data-driven, sparse, nonlinear mode...

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Detalles Bibliográficos
Autores principales: Cenedese, Mattia, Axås, Joar, Bäuerlein, Bastian, Avila, Kerstin, Haller, George
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8847615/
https://www.ncbi.nlm.nih.gov/pubmed/35169152
http://dx.doi.org/10.1038/s41467-022-28518-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8847615/
https://www.ncbi.nlm.nih.gov/pubmed/35169152
http://dx.doi.org/10.1038/s41467-022-28518-y

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