Deep learning for control of nonlinear optical systems

J. Nathan Kutz

doi:10.1117/12.2576998

5 March 2021 Deep learning for control of nonlinear optical systems

J. Nathan Kutz

Author Affiliations +

Proceedings Volume 11703, AI and Optical Data Sciences II; 1170318 (2021) https://doi.org/10.1117/12.2576998
Event: SPIE OPTO, 2021, Online Only

Abstract

We demonstrate the use of machine learning for adaptive control of nonlinear optical systems. From deep learning algorithms to nonlinear control methods, the optical sciences are an ideal platform for integrating data-driven control and machine learning for robust, self-tuning operation. For the specific case of mode-locked lasers, commercially available servo-controllers enact a training and execution software module capable of self tuning the laser cavity even in the presence of mechanical and/or environmental perturbations and discrepancies, thus providing algorithmic stabilization of mode-locking performance. The execution stage quickly stabilizes optimal mode-locking using various algorithmic innovations including (i) extremum seeking control, (ii) deep reinforcement learning and (iii) deep model predictive control. The demonstrated methods are robust and equation-free, thus requiring no detailed or quantitatively accurate model of the physics.

Citation Download Citation

J. Nathan Kutz "Deep learning for control of nonlinear optical systems", Proc. SPIE 11703, AI and Optical Data Sciences II, 1170318 (5 March 2021); https://doi.org/10.1117/12.2576998

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