<div class="csl-bib-body">
<div class="csl-entry">Aarts, G., Fukushima, K., Hatsuda, T., Ipp, A., Shi, S., Wang, L., & Zhou, K. (2025). Physics-driven learning for inverse problems in quantum chromodynamics. <i>Nature Reviews Physics</i>, <i>7</i>(3), 154–163. https://doi.org/10.1038/s42254-024-00798-x</div>
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dc.identifier.issn
2522-5820
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/225066
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dc.description.abstract
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. This is particularly relevant for quantum chromodynamics (QCD) — the theory of strong interactions — with its inherent challenges in interpreting observational data and demanding computational approaches. This Perspective highlights advances of physics-driven learning methods, focusing on predictions of physical quantities towards QCD physics and drawing connections to machine learning. Physics-driven learning can extract quantities from data more efficiently in a probabilistic framework because embedding priors can reduce the optimization effort. In the application of first-principles lattice QCD calculations and QCD physics of hadrons, neutron stars and heavy-ion collisions, we focus on learning physically relevant quantities, such as perfect actions, spectral functions, hadron interactions, equations of state and nuclear structure. We also emphasize the potential of physics-driven designs of generative models beyond QCD physics.
en
dc.language.iso
en
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dc.publisher
NATURE PORTFOLIO
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dc.relation.ispartof
Nature Reviews Physics
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dc.subject
Quantenchromodynamik
de
dc.subject
Inverses Problem
de
dc.subject
Deep Learning
en
dc.title
Physics-driven learning for inverse problems in quantum chromodynamics