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
QUANTA Magazine
New AI Strategy Mimics How Brains Learn to Smell
- Feature article on Delahunt & Kutz work: READ
- 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.
