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  Self-learning Machines based on Hamiltonian Echo Backpropagation

Lopez-Pastor, V., & Marquardt, F. (2023). Self-learning Machines based on Hamiltonian Echo Backpropagation. Physical Review X, 13(3): 031020. doi:10.1103/PhysRevX.13.031020.

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 Creators:
Lopez-Pastor, Victor1, Author
Marquardt, Florian1, Author           
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1Marquardt Division, Max Planck Institute for the Science of Light, Max Planck Society, ou_2421700              

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 Abstract: A physical self-learning machine can be defined as a nonlinear dynamical system that can be trained on data (similar to artificial neural networks), but where the update of the internal degrees of freedom that serve as learnable parameters happens autonomously. In this way, neither external processing and feedback nor knowledge of (and control of) these internal degrees of freedom is required. We introduce a general scheme for self-learning in any time-reversible Hamiltonian system. We illustrate the training of such a self-learning machine numerically for the case of coupled nonlinear wave fields.

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Language(s): eng - English
 Dates: 2023-08-18
 Publication Status: Published online
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 Identifiers: DOI: 10.1103/PhysRevX.13.031020
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Title: Physical Review X
  Abbreviation : Phys. Rev. X
Source Genre: Journal
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Publ. Info: New York, NY : American Physical Society
Pages: - Volume / Issue: 13 (3) Sequence Number: 031020 Start / End Page: - Identifier: Other: 2160-3308
CoNE: https://pure.mpg.de/cone/journals/resource/2160-3308