Digital Twins for Physical Systems
Course overview
Course overview of CSE: Digital Twins for Physical Systems
IBM defines “A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.” During this course, we will explore these concepts and their significance in addressing the challenges of monitoring and control of physical systems described by partial-differential equations. After introducing deterministic & statistical data assimilation techniques, the course switches gears towards scientific machine learning to introduce the technique of simulation-based inference, during which uncertainty is captured with generative conditional neural networks. We will use this capability of capturing conditional distributions to train neural networks to learn prior-to-posterior mappings central to data assimilation methods. We will also discuss how to control and make decisions under uncertainty. The course concludes by incorporating these techniques into uncertainty-aware Digital Twins to monitor and control complicated processes such as underground storage of CO2 or management of batteries.
Class meetings
| Meeting | Location | Time |
|---|---|---|
| Lecture | College of Skiles, Room 254 | Mon & Wed 5:00 - 6:15PM |
Prerequisites
Numerical Linear Algebra, Statistics, Machine Learning, Experience w/ Python, or Julia
Teaching team
| Name | Office hours | Location |
|---|---|---|
| Felix J. Herrmann (Instructor) | TBD | Zoom |
| Tuna Erdinc (TA) | TBD | Zoom |
| Shiqin Zeng (TA) | TBD | Zoom |
Access to Piazza
Students are encouraged to post their questions on Piazza on Canvas or Piazza direct, which will be monitored by Shiqin Zeng and Tuna Erdinc.