5650. Design Under Uncertainty and Health Prognostics

Explore probabilistic machine learning methods for engineering design and health prognostics. Provide students with a hands-on experience applying these methods to analyze and improve the reliability of engineered systems; applications of probabilistic machine learning methods to engineering design and health prognostics; hands-on learning of various probabilistic design methods, such as surrogate modeling, uncertainty quantification, reliability-based design, and robust design. Real-world examples of using probabilistic machine learning methods for fault diagnostics and health prognostics in two industrial applications: prognostics of implantable-grade Li-ion battery and deep learning and Industrial Internet of Things (IIoT) for predictive maintenance of industrial equipment.

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Last Refreshed: 19-DEC-25 05.20.12.054594 AM

Enrollment Data	Section	Class Number	Notes	Instructor	Enrollment	Session	Instruction Mode
1263 14002 1 001	001	14002		Hu, Chao	0/20	Reg	Online Asynchronous