Review on Physics-Driven Learning for Inverse Problems Published in Nature Reviews Physics
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
- 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