Cargando…
Model selection for dynamical systems via sparse regression and information criteria
We develop an algorithm for model selection which allows for the consideration of a combinatorially large number of candidate models governing a dynamical system. The innovation circumvents a disadvantage of standard model selection which typically limits the number of candidate models considered du...
Autores principales: | Mangan, N. M., Kutz, J. N., Brunton, S. L., Proctor, J. L. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society Publishing
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5582175/ https://www.ncbi.nlm.nih.gov/pubmed/28878554 http://dx.doi.org/10.1098/rspa.2017.0009 |
Ejemplares similares
-
Model selection for hybrid dynamical systems via sparse regression
por: Mangan, N. M., et al.
Publicado: (2019) -
Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
por: Kaiser, E., et al.
Publicado: (2018) -
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
por: Fasel, U., et al.
Publicado: (2022) -
SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics
por: Kaheman, Kadierdan, et al.
Publicado: (2020) -
Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control
por: Brunton, Steven L., et al.
Publicado: (2016)