Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session Y05: Machine Learning in Nonlinear Physics and Mechanics II
11:30 AM–1:54 PM,
Friday, March 19, 2021
Room: 05
Sponsoring
Units:
DSOFT GSNP DCOMP
Chair: Christopher Rycroft, Harvard University; Shmuel Rubinstein, Harvard University
Abstract: Y05.00004 : Self-learning machines based on time reversal
12:06 PM–12:18 PM
Live
Presenter:
Victor Lopez Pastor
(Max Planck Inst for Sci Light)
Authors:
Victor Lopez Pastor
(Max Planck Inst for Sci Light)
Florian Marquardt
(Max Planck Inst for Sci Light)
A self-learning machine can be defined as a physical 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, no knowledge of (and control of) these internal degrees of freedom is required. We introduce a general scheme to use any time-reversible Hamiltonian system as a self-learning machine. In particular, we show how this scheme can be applied to coupled nonlinear wave fields. We illustrate the training of such a self-learning machine numerically for the case of image recognition.
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