Short-wavelength reverberant wave systems for physical realization of reservoir computing

Machine learning (ML) has found widespread application over a broad range of important tasks. To enhance ML performance, researchers have investigated computational architectures whose physical implementations promise compactness, high-speed execution, physical robustness, and low-energy cost. Here,...

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Main Authors: Shukai Ma, Thomas M. Antonsen, Steven M. Anlage, Edward Ott
Format: Article
Language:English
Published: American Physical Society 2022-05-01
Series:Physical Review Research
Online Access:http://doi.org/10.1103/PhysRevResearch.4.023167
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author Shukai Ma
Thomas M. Antonsen
Steven M. Anlage
Edward Ott
author_facet Shukai Ma
Thomas M. Antonsen
Steven M. Anlage
Edward Ott
author_sort Shukai Ma
collection DOAJ
description Machine learning (ML) has found widespread application over a broad range of important tasks. To enhance ML performance, researchers have investigated computational architectures whose physical implementations promise compactness, high-speed execution, physical robustness, and low-energy cost. Here, we experimentally demonstrate an approach that uses the high sensitivity of reverberant short-wavelength waves for physical realization and enhancement of computational power of a type of ML known as reservoir computing (RC). The potential computation power of RC systems increases with their effective size. We here exploit the intrinsic property of short-wavelength reverberant wave sensitivity to perturbations to expand the effective size of the RC system by means of spatial and spectral perturbations. Working in the microwave regime, this scheme is tested experimentally on different ML tasks. Our results indicate the general applicability of reverberant wave based implementations of RC and of our effective reservoir size expansion techniques.
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spelling doaj.art-fc5fcc5f012f48dfbb007e2eb109d3f52024-04-12T17:21:24ZengAmerican Physical SocietyPhysical Review Research2643-15642022-05-014202316710.1103/PhysRevResearch.4.023167Short-wavelength reverberant wave systems for physical realization of reservoir computingShukai MaThomas M. AntonsenSteven M. AnlageEdward OttMachine learning (ML) has found widespread application over a broad range of important tasks. To enhance ML performance, researchers have investigated computational architectures whose physical implementations promise compactness, high-speed execution, physical robustness, and low-energy cost. Here, we experimentally demonstrate an approach that uses the high sensitivity of reverberant short-wavelength waves for physical realization and enhancement of computational power of a type of ML known as reservoir computing (RC). The potential computation power of RC systems increases with their effective size. We here exploit the intrinsic property of short-wavelength reverberant wave sensitivity to perturbations to expand the effective size of the RC system by means of spatial and spectral perturbations. Working in the microwave regime, this scheme is tested experimentally on different ML tasks. Our results indicate the general applicability of reverberant wave based implementations of RC and of our effective reservoir size expansion techniques.http://doi.org/10.1103/PhysRevResearch.4.023167
spellingShingle Shukai Ma
Thomas M. Antonsen
Steven M. Anlage
Edward Ott
Short-wavelength reverberant wave systems for physical realization of reservoir computing
Physical Review Research
title Short-wavelength reverberant wave systems for physical realization of reservoir computing
title_full Short-wavelength reverberant wave systems for physical realization of reservoir computing
title_fullStr Short-wavelength reverberant wave systems for physical realization of reservoir computing
title_full_unstemmed Short-wavelength reverberant wave systems for physical realization of reservoir computing
title_short Short-wavelength reverberant wave systems for physical realization of reservoir computing
title_sort short wavelength reverberant wave systems for physical realization of reservoir computing
url http://doi.org/10.1103/PhysRevResearch.4.023167
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