Explore an overview of Machine Learning applications in Fluid Mechanics through this 30-minute video lecture. Discover how fluid mechanics, one of the original "big data" sciences, has contributed to many advances in ML. Delve into topics such as orthogonal decomposition, low-dimensional patterns, boundary layer simulations, turbulent energy cascade, closure modeling, super-resolution, autoencoders, reduced order models, and flow control. Learn about the history of Machine Learning, including the AI Winter, and gain insights into patterns inspired by biology. Access additional resources, including related papers and the presenter's lab website, to further expand your understanding of this interdisciplinary field.
Overview
Syllabus
Introduction
What is Machine Learning
Machine Learning is not Magic
History of Machine Learning
AI Winter
Patterns
orthogonal decomposition
lowdimensional patterns
boundary layer simulations
turbulent energy cascade
closure modeling
superresolution
autoencoders
reduced order models
flow control
inspiration from biology
Taught by
Steve Brunton
