Reinforcement learning for tiled aperture beam combining (Conference Presentation)

Henrik Tünnermann; Akira Shirakawa

doi:10.1117/12.2545578

11 March 2020 Reinforcement learning for tiled aperture beam combining (Conference Presentation)

Henrik Tünnermann, Akira Shirakawa

Proceedings Volume 11260, Fiber Lasers XVII: Technology and Systems; 112600C (2020) https://doi.org/10.1117/12.2545578
Event: SPIE LASE, 2020, San Francisco, California, United States

Abstract

We developed a simulation of a beam tiled aperture combining system with n emitters. Using the Fourier propagation method, we calculate the far-field pattern depending on (n-1) relative phase parameters. We then simulate the phase noise as a time series using Gaussian random walk phase noise, with the possibility to actuate the phase parameters. For the stabilization scheme, we exclusively use a far filed intensity pattern. This image we feed through a simple two-layer neural network trained by a deep deterministic gradient reinforcement learning algorithm. This allows for a large amount of flexibility in terms of optimization metrics, which easily allows for beam combination in the far filed and beam shaping. The control policy is automatically derived from reward functions which can be phase patterns, target beam shapes or maximum power through a pinhole without the need for further optical analysis. After training only the image is used for stabilization.

Conference Presentation

Citation Download Citation

Henrik Tünnermann and Akira Shirakawa "Reinforcement learning for tiled aperture beam combining (Conference Presentation)", Proc. SPIE 11260, Fiber Lasers XVII: Technology and Systems, 112600C (11 March 2020); https://doi.org/10.1117/12.2545578

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KEYWORDS

Beam shaping

Automatic control

Detection and tracking algorithms

Evolutionary algorithms

Interferometry

Modulation

Neural networks

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