APPL527
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APPL527 - Physics-informed machine learning and engineering applications
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)
Cross Listed Courses
May be convened with
Name
Lecture
Workload Hours
3
Optional Component
No
Typically Offered Main Campus
Fall