Solving Inverse Problems with Physics-Driven Deep Learning

This talk kicks off a four-part seminar series on the DEEP-IN WG, an interdisciplinary working group exploring how modern deep learning — including deep generative models — can tackle inverse problems across scientific domains. In addition to DEEP-IN activities, I will present a new framework and vision, motivated by the growing synergy between physics-driven designs for deep learning and scientific discovery, as discussed in our recent review article. Future talks will demonstrate machine learning applications in collective behaviors, weather systems, and lattice field simulations.

This is an informal seminar, we will start with the methodology, give some practical examples, and finally reserve time for everyone interested to discuss it together.