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Physical learning beyond the quasistatic limit

  • Published:2022-05-16 Issue:2 Volume:4 Page:
  • ISSN:2643-1564
  • Container-title:Physical Review Research
  • language:en
  • Short-container-title:Phys. Rev. Research

Author:

Stern MenachemORCID,Dillavou Sam,Miskin Marc Z.,Durian Douglas J.ORCID,Liu Andrea J.ORCID

Funder

National Science Foundation

Simons Foundation

Materials Research Science and Engineering Center, Harvard University

Army Research Office

Air Force Office of Scientific Research

U.S. Department of Energy

Publisher

American Physical Society (APS)

Subject

General Engineering

Cited by 16 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Experimental demonstration of coupled learning in elastic networks;Physical Review Applied;2024-08-20

2. Physical neural networks with self-learning capabilities;Science China Physics, Mechanics & Astronomy;2024-07-22

3. Machine learning without a processor: Emergent learning in a nonlinear analog network;Proceedings of the National Academy of Sciences;2024-07-02

4. Training self-learning circuits for power-efficient solutions;APL Machine Learning;2024-02-27

5. Physical effects of learning;Physical Review E;2024-02-21
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