July 12 (Fri) at 10:00 - 11:30, 2024 (JST)
  • Liu Ziming (Ph.D. Student, Department of Physics, Massachusetts Institute of Technology, USA)
  • via Zoom
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).


  1. Ziming Liu and Max Tegmark, Machine learning conservation laws from trajectories, Phys. Rev. Lett. 126, 180604 (2021), doi: 10.1103/PhysRevLett.126.180604
  2. Ziming Liu and Max Tegmark, Machine learning hidden symmetries, Phys. Rev. Lett. 128, 180201 (2022), doi: 10.1103/PhysRevLett.128.180201
  3. 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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