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GEOPHYS 148/248: Machine Learning and the Physical Sciences

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Instructor: Ching-Yao Lai

This course provides a survey of the rapidly growing field of machine learning in the physical sciences. It covers various areas such as inverse problems, emulating physical processes, model discovery given data, and solution discovery given equations. It both introduces the background knowledge required to implement physics-informed deep learning and provides practical in-class coding exercises. Students have the opportunity to apply this emerging methodology to their own research interests across all fields of the physical sciences, including geophysics, climate, fluids, or other systems where the same technique applies. Students develop individual projects throughout the semester. Recommended Prerequisite: Calculus (e.g. Math 21), Differential Equations (e.g. MATH 53 or PHYSICS 111) or equivalents.