Spring 2026
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5650. Design Under Uncertainty and Health Prognostics
3.00 credits
Prerequisites: Not open to students who have passed ME 5895 when offered as Probabilistic Machine Learning for Engineering Design and Health Prognostics.
Grading Basis: Graded
Explores 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.
Last Refreshed: 06-FEB-26 05.20.15.789285 AM
| Enrollment Data | Section | Class Number | Notes | Instructor | Enrollment | Session | Instruction Mode |
|---|---|---|---|---|---|---|---|
| 1263 14002 1 001 | 001 | 14002 | Hu, Chao | 6/20 | Reg | Online Asynchronous |