Journal
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
Volume 475, Issue 2223, Pages -Publisher
ROYAL SOC
DOI: 10.1098/rspa.2018.0534
Keywords
model selection; hybrid systems; information criteria; sparse regression; nonlinear dynamics; data-driven discovery
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Funding
- Bill and Melinda Gates through the Global Good Fund
- Air Force Office of Scientific Research [FA9550-15-1-0385, FA9550-17-1-0329]
- Defense Advanced Research Projects Agency (DARPA) [HR0011-16-C-0016]
- Army Research Office [W911NF-17-1-0422]
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Hybrid systems are traditionally difficult to identify and analyse using classical dynamical systems theory. Moreover, recently developed model identification methodologies largely focus on identifying a single set of governing equations solely from measurement data. In this article, we develop a new methodology, Hybrid-Sparse Identification of Nonlinear Dynamics, which identifies separate nonlinear dynamical regimes, employs information theory to manage uncertainty and characterizes switching behaviour. Specifically, we use the nonlinear geometry of data collected from a complex system to construct a set of coordinates based on measurement data and augmented variables. Clustering the data in these measurement-based coordinates enables the identification of nonlinear hybrid systems. This methodology broadly empowers nonlinear system identification without constraining the data locally in time and has direct connections to hybrid systems theory. We demonstrate the success of this method on numerical examples including a mass-spring hopping model and an infectious disease model. Characterizing complex systems that switch between dynamic behaviours is integral to overcoming modern challenges such as eradication of infectious diseases, the design of efficient legged robots and the protection of cyber infrastructures.
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