4.8k citations · 6.6k across the 13 of their papers we have counts for
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Data-driven discovery of partial differential equations
Samuel H. Rudy, Steven L. Brunton, Joshua L. Proctor +1
We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The…
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…
Koopman Theory for Partial Differential Equations
J. Nathan Kutz, Joshua L. Proctor, Steven L. Brunton
We consider the application of Koopman theory to nonlinear partial differential equations. We demonstrate that the observables chosen for constructing the Koopman operator are crit…
Inferring biological networks by sparse identification of nonlinear dynamics
Niall M. Mangan, Steven L. Brunton, Joshua L. Proctor +1
Inferring the structure and dynamics of network models is critical to understanding the functionality and control of complex systems, such as metabolic and regulatory biological ne…
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…
Generalizing Koopman Theory to allow for inputs and control
Joshua L. Proctor, Steven L. Brunton, J. Nathan Kutz
We develop a new generalization of Koopman operator theory that incorporates the effects of inputs and control. Koopman spectral analysis is a theoretical tool for the analysis of…