Machine Learning For Fluid Mechanics - Steven Brunton | Podcast #50

Dr. Steven Brunton's research focuses on combining techniques in dimensionality reduction, sparse sensing, and machine learning for the data-driven discovery and control of complex dynamical systems. He is also interested in how low-rank coherent patterns that underlie high-dimensional data facilitate sparse measurements and optimal sensor and actuator placement for control. He is developing adaptive controllers in an equation-free context using machine learning. Specific applications in fluid dynamics include closed-loop turbulence control for mixing enhancement, bio-locomotion, and renewable energy. Other applications include neuroscience, medical data analysis, networked dynamical systems, and optical systems.

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