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.

Consider the following scenario: a student comes to the office hour to ask questions about a physics homework problem they struggled with. The instructor smiles and says, “this problem is trivial”.