This is a hybrid course to introduce you to active research topics in physics informed machine learning, including Forward Problem, Inverse Problem and so on. For each topic, i will give a introduction and the goal is to provide a sufficient overview of problems in physics informed machine learning to enable further reading and an informed decision regarding the course project topic. You will work on the project through the semester as part of a team of 2-3 students (depending on enrollment).
After the introductory classes, you will read and review a paper (listed in the schedule) prior to each class. Each lecture will start with a discussion of the paper that was reviewed.
If you are discussion lead: Please make PPT slides for the discussion of about 10 pages, covering the Motivation, Related literature, Methodology, as well as your thoughts(review) on the paper. Following the paper discussion, all students will participants in discussion of potential research and applications.
If you are not the discussion lead, but you are still expected to read the paper before class and submit review, following here.
In addition, We will have everyone updates on their project 2~3 times over the course of the semester. At the end of the semester, teams will give final project presentations.
Feedback is very welcome. If you have any questions or concerns about the class or the requirements, please be sure to discuss them with the instructor early on.
Summary:
15 paper reviews: I will try to let you read 1 paper per week, for the paper that the instructor leads discussion, no need to submit reviews.
1 paper discussion lead
2 project presentations (idea proposal and final presentation)
1 project update check-in
NOTE: There will be no final exam!
Auditing the class: Students are required to submit reviews for 3 papers on the topics of PINN, FNO and DeepONet. Students should attend all lectures and participate in discussions. Students need not lead discussions or do a project.