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
- B. Lusch, J. N. Kutz and S. L. Brunton, Deep learning for universal linear embeddings of nonlinear dynamics, Nature Communications 4950 (2018).
- C. Delahunt and J. N. Kutz, Insect cyborgs: Biological feature generators improve machine learning accuracy on limited data, arxiv:1808.08124 (2018).
- C. Delahunt and J. N. Kutz, Putting a bug in ML: the moth olfactory network learns to read MNIST, arxiv:1802.05405 (2018).
- S. Rudy, J. N. Kutz and S. L. Brunton, Deep learning of dynamics and signal-noise decomposition with time-stepping constraints, arxiv:1808.02578 (2018).
- T. Baumeister, S. L. Brunton and J. N. Kutz, Deep learning and model predictive control for self-tuning mode-locked lasers, J. Opt. Soc. Am. B 35, 617-626 (2018).
