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物理学与深度学习:2024年诺贝尔物理学奖介绍

唐泽宸, 段文晖, 徐勇

唐泽宸, 段文晖, 徐勇. 物理学与深度学习:2024年诺贝尔物理学奖介绍[J]. 物理, 2025, 54(1): 1-9. DOI: 10.7693/wl20250101
引用本文: 唐泽宸, 段文晖, 徐勇. 物理学与深度学习:2024年诺贝尔物理学奖介绍[J]. 物理, 2025, 54(1): 1-9. DOI: 10.7693/wl20250101
TANG Ze-Chen, DUAN Wen-Hui, XU Yong. Physics and deep learning:an introduction to the 2024 Nobel Prize in Physics[J]. PHYSICS, 2025, 54(1): 1-9. DOI: 10.7693/wl20250101
Citation: TANG Ze-Chen, DUAN Wen-Hui, XU Yong. Physics and deep learning:an introduction to the 2024 Nobel Prize in Physics[J]. PHYSICS, 2025, 54(1): 1-9. DOI: 10.7693/wl20250101
唐泽宸, 段文晖, 徐勇. 物理学与深度学习:2024年诺贝尔物理学奖介绍[J]. 物理, 2025, 54(1): 1-9. CSTR: 32040.14.wl20250101
引用本文: 唐泽宸, 段文晖, 徐勇. 物理学与深度学习:2024年诺贝尔物理学奖介绍[J]. 物理, 2025, 54(1): 1-9. CSTR: 32040.14.wl20250101
TANG Ze-Chen, DUAN Wen-Hui, XU Yong. Physics and deep learning:an introduction to the 2024 Nobel Prize in Physics[J]. PHYSICS, 2025, 54(1): 1-9. CSTR: 32040.14.wl20250101
Citation: TANG Ze-Chen, DUAN Wen-Hui, XU Yong. Physics and deep learning:an introduction to the 2024 Nobel Prize in Physics[J]. PHYSICS, 2025, 54(1): 1-9. CSTR: 32040.14.wl20250101

物理学与深度学习:2024年诺贝尔物理学奖介绍

基金项目: 

国家重点研发计划(批准号:2023YFA1406400;2024YFA1409100)、国家自然科学基金基础科学中心(批准号:52388201)、国家自然科学基金(批准号:12334003;12421004;12361141826)、国家科技重大专项(批准号:2023ZD0300500)、国家杰出青年科学基金(批准号:12025405)资助项目

详细信息
    通讯作者:

    徐勇,email:yongxu@mail.tsinghua.edu.cn

Physics and deep learning:an introduction to the 2024 Nobel Prize in Physics

  • 摘要: 2024年诺贝尔物理学奖授予神经网络相关的研究工作,充分肯定了以人工神经网络为代表的深度学习方法在多学科交叉前沿中的变革性影响。物理学家约翰·霍普菲尔德与“AI教父”杰弗里·辛顿因其在人工神经网络发展史上的杰出贡献荣膺此奖,引发了学术界的广泛关注与深入讨论。文章将从物理学研究者的视角,解读两位诺奖得主的代表性研究成果,探讨物理学与深度学习的紧密联系,分析物理学在推动深度学习发展中的启示性作用。并以深度学习与第一性原理计算方法的结合为例,展望深度学习对物理学未来发展的深远影响。
    Abstract: The 2024 Nobel Prize in Physics was awarded for pioneering research on neural networks, recognizing the transformative impact of deep learning across interdisciplinary fields. Physicist John Hopfield and“Godfather of AI”Geoffrey Hinton were honored for their outstanding contributions to the development of artificial neural networks. This article, written from the perspective of physics researchers, will highlight the representative research achievements of the laureates, and explore the deep connection between physics and deep learning. The essential role of physics in advancing deep learning will be examined, and the profound impact of deep learning on the future development of physics will be envisioned, using its integration with first-principles calculations as a concrete example.
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出版历程
  • 收稿日期:  2024-12-22
  • 网络出版日期:  2025-01-16

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