物理学与深度学习:2024年诺贝尔物理学奖介绍

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

物理学与深度学习:2024年诺贝尔物理学奖介绍

    通讯作者: 徐勇,email:yongxu@mail.tsinghua.edu.cn
  • 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

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。
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  • 收稿日期:  2024-12-23

物理学与深度学习:2024年诺贝尔物理学奖介绍

    通讯作者: 徐勇,email:yongxu@mail.tsinghua.edu.cn
  • 清华大学物理系 北京 100084
基金项目: 

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。

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