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arXiv · 2609.38711

Advancing Nuclear Physics with Machine Learning and Artificial Intelligence

Wanbing He·Qingfeng Li·Yugang Ma·Zhongming Niu·Junchen Pei·Yingxun Zhang
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Abstract

Machine learning (ML) and artificial intelligence (AI) are becoming powerful tools in scientific research across various disciplines. In this review, we summarize recent progress in AI-assisted studies of nuclear structure and reaction observables, heavy-ion collisions and dense nuclear matter properties, many-body wave functions, experimental facilities and data analysis. This review focus on new progress since the last review in 2023, and machine learning in nuclear physics is evolving from purely data inferences to physics informed learning. Future directions on the integration of physical knowledge with modern learning architectures, large foundation models and other emerging methods are also reviewed. These developments suggest that AI is enabling and advancing new approaches towards most challenging nuclear physics problems.

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Wanbing He, Qingfeng Li, Yugang Ma, Zhongming Niu, Junchen Pei, Yingxun Zhang. 2026-09-30. Advancing Nuclear Physics with Machine Learning and Artificial Intelligence. https://arxiv.org/abs/2609.38711

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