Data-Driven Dynamical Systems


We develop data methods for reduced-order and equation-free modeling, machine learning, and compressive sensing for applications across the engineering, physical and biological sciences. We also leverage traditional applied mathematics expertise in nonlinear waves, scientific computing, perturbation and asymptotic methods, and bifurcation theory. Domain science research includes optics, neuroscience, computer vision, and fluid dynamics.

Recent Video Abstract


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This video highlights recent innovations for discovering nonlinear dynamical systems from time series data.

News and Announcements


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  • Machine learning to discover physics: HEAR
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  • Neurosensory network functionality (SIAM Life Sciences): HEAR


  Some Recent Papers [ Google Scholar Profile]


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