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
Data-Driven Modeling & Scientific Computation (Second Edition)
- M. Gao, J. Williams and J. N. Kutz, Sparse identification of nonlinear dynamics and Koopman operators with shallow recurrent decoders, PNAS (2026).
- M. Tomasetto, A. Mazoni and J. N. Kutz, Reduced order modeling with shallow recurrent decoders, Nature Communications (2026).
- R. Raut and others and J. N. Kutz, Arousal as a universal embedding for spatiotemporal brain dynamics, Nature (2025).
News and Announcements
On-Leave: Autodesk Research, London UK