Chaos as an intermittently forced linear system
The huge amount of data generated in fields like neuroscience or finance calls for effective strategies that mine data to reveal underlying dynamics. Here Brunton et al.develop a data-driven technique to analyze chaotic systems and predict their dynamics in terms of a forced linear model.
Saved in:
Main Authors: | Steven L. Brunton, Bingni W. Brunton, Joshua L. Proctor, Eurika Kaiser, J. Nathan Kutz |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2017
|
Subjects: | |
Online Access: | https://doaj.org/article/ae9e7d04e6aa4c9f8cd998dd33b77dcc |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Deep learning for universal linear embeddings of nonlinear dynamics
by: Bethany Lusch, et al.
Published: (2018) -
Learning dominant physical processes with data-driven balance models
by: Jared L. Callaham, et al.
Published: (2021) -
DeepGreen: deep learning of Green’s functions for nonlinear boundary value problems
by: Craig R. Gin, et al.
Published: (2021) -
Imaging the Transport Dynamics of Single Alphaherpesvirus Particles in Intact Peripheral Nervous System Explants from Infected Mice
by: Andrea E. Granstedt, et al.
Published: (2013) -
Wing structure and neural encoding jointly determine sensing strategies in insect flight.
by: Alison I Weber, et al.
Published: (2021)