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Proceedings Paper

Optical reservoir computing for high-dimensional spatio-temporal chaotic systems prediction (Conference Presentation)
Author(s): Mushegh Rafayelyan; Jonathan Dong; Yongqi Tan; Florent Krzakala; Sylvain Gigan

Paper Abstract

There have been a number of rapid advances in the prediction of the dynamics of chaotic systems using a technique known as Reservoir Computing. These techniques are mostly not effective for large networks, as the complexity of the task increases quadratically both in time and memory. We report new advances in Optical Reservoir Computing using multiple light scattering to accelerate the recursive computation of the reservoir states. Different approaches to information encoding based on phase or amplitude spatial light modulations are compared. We demonstrate the scalability and the good prediction performance of our approach using the Kuramoto-Sivashinsky equation as an example of a spatiotemporally chaotic system.

Paper Details

Date Published: 9 March 2020
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Proc. SPIE 11299, AI and Optical Data Sciences, 112990B (9 March 2020); doi: 10.1117/12.2545755
Show Author Affiliations
Mushegh Rafayelyan, Lab. Kastler Brossel, Ecole Normale Supérieure, Univ. de Recherche Paris Sciences et Lettres, CNRS (France)
Sorbonne Univ. (France)
Collège de France (France)
Jonathan Dong, Lab. Kastler Brossel, Ecole Normale Supérieure, Univ. de Recherche Paris Sciences et Lettres, CNRS (France)
Sorbonne Univ. (France)
Collège de France (France)
Yongqi Tan, Lab. Kastler Brossel, Ecole Normale Supérieure, Univ. de Recherche Paris Sciences et Lettres, CNRS (France)
Sorbonne Univ. (France)
Collège de France (France)
Florent Krzakala, Lab. de physique de l'ENS, CNRS (France)
Univ. de Recherche Paris Sciences et Lettres (France)
Sorbonne Univ. (France)
Sylvain Gigan, Lab. Kastler Brossel, Ecole Normale Supérieure, Univ. de Recherche Paris Sciences et Lettres, CNRS (France)
Sorbonne Univ. (France)
Collège de France (France)


Published in SPIE Proceedings Vol. 11299:
AI and Optical Data Sciences
Bahram Jalali; Ken-ichi Kitayama, Editor(s)

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