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      Deep Learning and Model Predictive Control for Self-Tuning Mode-Locked Lasers

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          Abstract

          Self-tuning optical systems are of growing importance in technological applications such as mode-locked fiber lasers. Such self-tuning paradigms require {\em intelligent} algorithms capable of inferring approximate models of the underlying physics and discovering appropriate control laws in order to maintain robust performance for a given objective. In this work, we demonstrate the first integration of a {\em deep learning} (DL) architecture with {\em model predictive control} (MPC) in order to self-tune a mode-locked fiber laser. Not only can our DL-MPC algorithmic architecture approximate the unknown fiber birefringence, it also builds a dynamical model of the laser and appropriate control law for maintaining robust, high-energy pulses despite a stochastically drifting birefringence. We demonstrate the effectiveness of this method on a fiber laser which is mode-locked by nonlinear polarization rotation. The method advocated can be broadly applied to a variety of optical systems that require robust controllers.

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          Author and article information

          Journal
          02 November 2017
          Article
          1711.02702
          5e0a2234-49e7-4edb-b7db-5e2b5af63a7a

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          9 pages, 6 figures
          cs.LG nlin.PS

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