The integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems, in which accurate physical properties are extracted from complex observations.

As a physics major, it feels like I spend the majority of my waking life solving problems. I’ve calculated the amount of water you get from mixing different ratios of steam and ice, the path of ...

Using a conventional computer and cutting-edge mathematical tools and code, physicists at the Center for Computational Quantum Physics (CCQ) at the Simons Foundation's Flatiron Institute and ...