Discovering Physical Laws with Artificial Intelligence
- 日時
- 2024年7月12日(金)10:00 - 11:30 (JST)
- 講演者
-
- 刘 子鸣 (Ph.D. Student, Department of Physics, Massachusetts Institute of Technology, USA)
- 会場
- via Zoom
- 言語
- 英語
- ホスト
- Lingxiao Wang
Deep neural networks have been extremely successful in language and vision tasks. However, their black-box nature makes them undesirable for scientific tasks. In this talk, I will show how we can make these black-box AI models more interpretable and transparent and use them to discover physical laws, including conservation laws (AI Poincare), symmetries, phase transitions and symbolic relations (Kolmogorov-Arnold Networks).
Ziming is a physicist and a machine learning researcher. Ziming received BS in physics from Peking Univeristy in 2020, and is current a fourth-year PhD student at MIT and IAIFI, advised by Max Tegmark. His research interests lie generally in the intersection of artificial intelligence (AI) and physics (science in general).
References
- Ziming Liu and Max Tegmark, Machine learning conservation laws from trajectories, Phys. Rev. Lett. 126, 180604 (2021), doi: 10.1103/PhysRevLett.126.180604
- Ziming Liu and Max Tegmark, Machine learning hidden symmetries, Phys. Rev. Lett. 128, 180201 (2022), doi: 10.1103/PhysRevLett.128.180201
- Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljačić, Thomas Y. Hou, Max Tegmark, KAN: Kolmogorov-Arnold Networks, doi: 10.48550/arXiv.2404.19756, arXiv: 2404.19756
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