Model selection for hybrid dynamical systems via sparse regression

N. M. Mangan, T. Askham, S. L. Brunton, J. N. Kutz, J. L. Proctor

Research output: Contribution to journalArticlepeer-review

60 Scopus citations


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.

Original languageEnglish (US)
Article number20180534
JournalProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
Issue number2223
StatePublished - Mar 1 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Mathematics
  • General Engineering
  • General Physics and Astronomy


  • Data-driven discovery
  • Hybrid systems
  • Information criteria
  • Model selection
  • Nonlinear dynamics
  • Sparse regression


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