Optical reservoir computing for high-dimensional spatio-temporal chaotic systems prediction (Conference Presentation)

Mushegh Rafayelyan; Jonathan Dong; Yongqi Tan; Florent Krzakala; Sylvain Gigan

doi:10.1117/12.2545755

9 March 2020 Optical reservoir computing for high-dimensional spatio-temporal chaotic systems prediction (Conference Presentation)

Mushegh Rafayelyan, Jonathan Dong, Yongqi Tan, Florent Krzakala, Sylvain Gigan

Author Affiliations +

Proceedings Volume 11299, AI and Optical Data Sciences; 112990B (2020) https://doi.org/10.1117/12.2545755
Event: SPIE OPTO, 2020, San Francisco, California, United States

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.

Conference Presentation

Citation Download Citation

Mushegh Rafayelyan, Jonathan Dong, Yongqi Tan, Florent Krzakala, and Sylvain Gigan "Optical reservoir computing for high-dimensional spatio-temporal chaotic systems prediction (Conference Presentation)", Proc. SPIE 11299, AI and Optical Data Sciences, 112990B (9 March 2020); https://doi.org/10.1117/12.2545755

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