Cargando…
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
Sparse model identification enables the discovery of nonlinear dynamical systems purely from data; however, this approach is sensitive to noise, especially in the low-data limit. In this work, we leverage the statistical approach of bootstrap aggregating (bagging) to robustify the sparse identificat...
Autores principales: | Fasel, U., Kutz, J. N., Brunton, B. W., Brunton, S. L. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9006119/ https://www.ncbi.nlm.nih.gov/pubmed/35450025 http://dx.doi.org/10.1098/rspa.2021.0904 |
Ejemplares similares
-
SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics
por: Kaheman, Kadierdan, et al.
Publicado: (2020) -
Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
por: Kaiser, E., et al.
Publicado: (2018) -
Data-driven discovery of coordinates and governing equations
por: Champion, Kathleen, et al.
Publicado: (2019) -
Model selection for dynamical systems via sparse regression and information criteria
por: Mangan, N. M., et al.
Publicado: (2017) -
Model selection for hybrid dynamical systems via sparse regression
por: Mangan, N. M., et al.
Publicado: (2019)