High Energy Physics (HEP) is a deeply collaborative and software-driven discipline, where scientific discovery depends on advanced computing, data analysis, ...

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.

It has been shown that active learning methods are more effective than traditional lecturing at improving student conceptual understanding and reducing failure rates in undergraduate physics courses.

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...

As artificial intelligence explodes in popularity, two of its pioneers have nabbed the 2024 Nobel Prize in physics. The prize surprised many, as these developments are typically associated with ...

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 ...