Lessons on interpretable machine learning from particle physics

被引:0
|
作者
Christophe Grojean
Ayan Paul
Zhuoni Qian
Inga Strümke
机构
[1] Deutsches Elektronen-Synchrotron DESY,Institut für Physik
[2] Humboldt Universität zu Berlin,School of Physics
[3] Hangzhou Normal University,Department of Computer Science
[4] Norwegian University of Science and Technology,undefined
来源
Nature Reviews Physics | 2022年 / 4卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Machine learning methods have proved powerful in particle physics, but without interpretability there is no guarantee the outcome of a learning algorithm is correct or robust. Christophe Grojean, Ayan Paul, Zhuoni Qian and Inga Strümke give an overview of how to introduce interpretability to methods commonly used in particle physics.
引用
收藏
页码:284 / 286
页数:2
相关论文
共 50 条
  • [1] Lessons on interpretable machine learning from particle physics
    Grojean, Christophe
    Paul, Ayan
    Qian, Zhuoni
    Strumke, Inga
    [J]. NATURE REVIEWS PHYSICS, 2022, 4 (05) : 284 - 286
  • [2] Machine Learning in Particle Physics
    Purohit, Milind, V
    [J]. BIG DATA ANALYTICS IN ASTRONOMY, SCIENCE, AND ENGINEERING, BDA 2023, 2024, 14516 : 128 - 138
  • [3] Machine and deep learning applications in particle physics
    Bourilkov, Dimitri
    [J]. INTERNATIONAL JOURNAL OF MODERN PHYSICS A, 2019, 34 (35):
  • [4] Wind Farm Modeling with Interpretable Physics-Informed Machine Learning
    Howland, Michael F.
    Dabiri, John O.
    [J]. ENERGIES, 2019, 12 (14)
  • [5] Machine learning for anomaly detection in particle physics
    Belis, Vasilis
    Odagiu, Patrick
    Aarrestad, Thea Klaeboe
    [J]. Reviews in Physics, 2024, 12
  • [6] Investigating the Physics of Tokamak Global Stability with Interpretable Machine Learning Tools
    Murari, Andrea
    Peluso, Emmanuele
    Lungaroni, Michele
    Rossi, Riccardo
    Gelfusa, Michela
    [J]. APPLIED SCIENCES-BASEL, 2020, 10 (19):
  • [7] Hessian-based toolbox for reliable and interpretable machine learning in physics
    Dawid, Anna
    Huembeli, Patrick
    Tomza, Michal
    Lewenstein, Maciej
    Dauphin, Alexandre
    [J]. MACHINE LEARNING-SCIENCE AND TECHNOLOGY, 2022, 3 (01):
  • [8] Anisotropic physics-regularized interpretable machine learning of microstructure evolution
    Melville, Joseph
    Yadav, Vishal
    Yang, Lin
    Krause, Amanda R.
    Tonks, Michael R.
    Harley, Joel B.
    [J]. COMPUTATIONAL MATERIALS SCIENCE, 2024, 238
  • [9] Interpretable Machine Learning
    Chen, Valerie
    Li, Jeffrey
    Kim, Joon Sik
    Plumb, Gregory
    Talwalkar, Ameet
    [J]. Queue, 2021, 19 (06): : 28 - 56
  • [10] A novel physics-regularized interpretable machine learning model for grain growth
    Yan, Weishi
    Melville, Joseph
    Yadav, Vishal
    Everett, Kristien
    Yang, Lin
    Kesler, Michael S.
    Krause, Amanda R.
    Tonks, Michael R.
    Harley, Joel B.
    [J]. MATERIALS & DESIGN, 2022, 222