2025-03-06 Research News

Nature Reviews Physics recently published a review paper on ”Physics-driven learning for inverse problems in quantum chromodynamics”, from DEEP-IN working group led by Lingxiao Wang (Research Scientist, iTHEMS), including Tetsuo Hatsuda (Program Director, iTHEMS). The authors explore how combining deep learning techniques with physics-driven designs significantly improves the extraction of accurate physical properties from complex QCD phenomena. Highlighting applications such as lattice QCD calculations and studies of hadron interactions, neutron stars, and heavy-ion collisions, the review emphasizes the benefits of embedding physical priors into machine learning models and suggests broader potentials to general physics problems.

Reference

  1. Gert Aarts, Kenji Fukushima, Tetsuo Hatsuda, Andreas Ipp, Shuzhe Shi, Lingxiao Wang & Kai Zhou, Physics-driven learning for inverse problems in quantum chromodynamics, Nat Rev Phys 7, 154–163 (2025), doi: 10.1038/s42254-024-00798-x

Related Link