I am very happy to share that I delivered a 3-hour short course on Scientific Machine Learning (SciML) for computational physics at the ASME Summer Heat Transfer Conference (SHTC 2024) this week. https://event.asme.org/SHTC/Program/Short-Courses
During the course, we explored:
- Using physics to regularize ML training and inform neural architecture design
- Integrating ML with traditional numerical solvers using differentiable programming
- Leveraging generative AI for modeling stochastic spatiotemporal processes
It was wonderful to see so many participants and to connect with researchers and students.
As requested, the slides from the tutorial are now available here https://www.dropbox.com/scl/fi/bp6inwy5kfp6rsg0oksrt/SciML-tutorial-Wang-public.pdf?rlkey=6bxmnigpnvc6w8pqfcaxcuns5&dl=0.
Thank you to everyone who attended and contributed to the discussions!