Data-Driven Dynamical Systems
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Connectomic Dynamics and Neuro-Sensory Integration
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Machine Learning for Self-Tuning Mode-Locked Lasers
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Lightboard Technology for Online Education
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Data-Driven Modeling Textbook
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Light Bullet Generation in Waveguide Arrays
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Compressive Sensing for Characterizing Fluid Flow
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Dynamic Mode Decomposition for ECOG Data
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Fluid Dynamics at Cafe Kutz
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Application of Classical Methods of Applied Mathematics
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Cognitive Disfunction from Neurodegenerative Diseases
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Odor Tracking and Sensorimotor Integration
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Retinal Waves on Starburst Amacrine Cells
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Beam Steering for Metamaterial Antennas
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Intracavity Dynamics in High-Power Fiber Lasers
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Compressive Sensing for Characterizing Complex Systems
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Reduced-Order Modeling for Bifurcation Studies of Water Waves
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Multi-Phase Flows at Cafe Kutz
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.
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News and Announcements
- Check out the arxiv paper on Model selection for dynamical systems via sparse regression and information criteria
- We just put an arxiv paper up on Data-driven forecast of Dengue outbreaks in Brazil: A critical assessment of climate conditions for different capitals
- S. Brunton, J. N. Kutz and J. Proctor, Data-driven Discovery of Governing Physical Laws, SIAM News 50:1 (2017).
- S. Brunton, B. Brunton, J. L. Proctor, E. Kaiser and J. N. Kutz, Chaos as an intermittently forced linear system, Nature Communications 8:19 (2017).
- S. Rudy, S. Brunton, J. L. Proctor and J. N. Kutz, Data-driven discovery of partial differential equations, Science Advances 3 e1602614 (2017).
- S. Brunton, J. Proctor and J. N. Kutz, Discovering governing equations from data by sparse identification of nonlinear dynamical systems, Proceedings of the National Academy of Sciences 113 (2016) 3932-3937.
