4.8k citations
- University of WashingtonUS11 papers
- Gates FoundationUS3 papers
- Center for Astrophysics Harvard & SmithsonianUS2 papers
- Monash UniversityAU2 papers
- New Mexico State UniversityUS2 papers
- Northwestern UniversityUS2 papers
- University of TorontoCA2 papers
- University of VermontUS2 papers
- American Institute of Aeronautics and AstronauticsUS1 paper
- California Institute of TechnologyUS1 paper
- Case Western Reserve UniversityUS1 paper
- Centre de Recerca MatemàticaES1 paper
5 papers · 1 filter
Model selection for hybrid dynamical systems via sparse regression
Niall M Mangan, Travis Askham, Steven L Brunton +2
Hybrid systems are traditionally difficult to identify and analyze using classical dynamical systems theory. Moreover, recently developed model identification methodologies largely…
Chaos as an Intermittently Forced Linear System
Steven L. Brunton, Bingni W. Brunton, Joshua L. Proctor +2
Understanding the interplay of order and disorder in chaotic systems is a central challenge in modern quantitative science. We present a universal, data-driven decomposition of cha…
Sparse Identification of Nonlinear Dynamics with Control (SINDYc)
Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz
Identifying governing equations from data is a critical step in the modeling and control of complex dynamical systems. Here, we investigate the data-driven identification of nonlin…
Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control
Steven L. Brunton, Bingni W. Brunton, Joshua L. Proctor +1
In this work, we explore finite-dimensional linear representations of nonlinear dynamical systems by restricting the Koopman operator to an invariant subspace. The Koopman operator…
Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz
The ability to discover physical laws and governing equations from data is one of humankind's greatest intellectual achievements. A quantitative understanding of dynamic constraint…