Data-Driven Dynamical Systems


Our group develops the data methods of reduced-order and equation-free modeling, machine learning, and compressive sensing for applications across the engineering, physical and biological sciences. These methods leverage traditional applied mathematics expertise in nonlinear waves, scientific computing, perturbation and asymptotic methods, and bifurcation theory. Domain science research includes optics, neuroscience, video analysis, and fluid dynamics.

Recent Video Abstract


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This video highlights the recent innovation of multi-resolution analysis applied to dynamic mode decomposition.
Watch on YouTube


  Three Most Recent Papers [ Google Scholar Profile]


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