Machine Learning for Fluid Mechanics

This course covers the application of Machine Learning in Fluid Mechanics, highlighting how ML techniques are utilized in this field. The learning outcomes include understanding the history of Machine Learning, exploring techniques such as orthogonal decomposition and autoencoders, and applying ML in areas like flow control and turbulent energy cascade modeling. The course teaches skills in pattern recognition, low-dimensional pattern analysis, and building reduced order models. The teaching method involves presenting theoretical concepts and practical applications in a concise video format. The intended audience for this course includes students, researchers, and professionals interested in the intersection of Machine Learning and Fluid Mechanics.