APPL527
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APPL527 - Physics-informed machine learning and engineering applications
Course ID
042596
Course Description
The course will familiarize students with the emerging ideas of integrating physics with machine learning for applications in engineering. The topics will include (i) Bayesian modeling and uncertainty quantification with physics-based theoretical models and data; (ii) integrating machine learning with physics-based numerical models and simulations, (iii) physics-informed neural networks.
Min Units
3
Max Units
3
Repeatable for Credit
No
Grading Basis
GRD - Regular Grades A, B, C, D, E
Career
Graduate
Course Attributes
CE - CL (Cross Listed)
Course Requisites
Working knowledge of Python or MATLAB.
Cross Listed Courses
May be convened with
Component
Lecture
Optional Component
No