Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session N09: Predicting Nonlinear and Complex Systems with Machine Learning II
11:30 AM–2:30 PM,
Wednesday, March 16, 2022
Room: McCormick Place W-180
Sponsoring
Units:
GSNP DSOFT DCOMP
Chair: Ying-Cheng Lai, Arizona State University
Abstract: N09.00002 : Physical Reservoir Computing with Over-Moded Complex Systems*
12:06 PM–12:18 PM
Presenter:
Shukai Ma
(University of Maryland, College Park)
Authors:
Shukai Ma
(University of Maryland, College Park)
Thomas M Antonsen
(University of Maryland, College Park)
Steven M Anlage
(University of Maryland, College Park)
Edward Ott
(University of Maryland, College Park)
Here we utilize the wave dynamics inside a chaotic-shaped electromagnetic cavity containing nonlinear elements to emulate the complex dynamics of reservoir computing (RC), which is a genre of recurrent neural network (RNN). In the short-wavelength limit, the evolution of electromagnetic fields inside chaotic-shaped enclosures shows complex patterns, and is extremely sensitive to perturbations. We propose unique techniques to create virtual RC nodes by both frequency stirring and spatial perturbation. The computational power of the wave chaotic RC is demonstrated with various benchmark tasks. The wave chaos RC is also mechanically robust, and can be scaled to a wide range of wavelengths from RF to the visible.
*This work was supported by ONR under Grant No. N000141912481, AFOSR COE Grant FA9550-15-1-0171, and the Maryland Quantum Materials Center.
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