For many undergraduate students, exploring the complexities of physics for the first time, from wading through advanced mathematics, to absorbing information in a large lecture format, can be a ...

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.

Advances in machine learning (ML) and deep learning (DL) are undoubtedly enabling significant breakthroughs in all areas of science and technology. ML/DL models, however, do not necessarily obey the ...