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.

Standard physics informed deep learning and their enhanced variants encounter challenges in addressing inverse problems characterized by extreme discontinuities and high-order parameterized ...