Numerical and experimental study on coherent beam combining using an improved stochastic parallel gradient descent algorithm. (26th June 2020)

• Record Type: Journal Article
• Authors: Song, Jikun; Li, Yuanyang; Che, Dongbo; Wang, Tingfeng
• Is Part Of: Laser physics. Volume 30:Number 8(2020)
• Publication Date: 2020-06-26
• Subjects: coherent beam combining -- adaptive gradient -- stochastic parallel gradient descent algorithm; Lasers -- Periodicals; 621.36605
• DOI: 10.1088/1555-6611/ab9118
• Languages: English
• ISSNs: 1054-660X

Abstract

Abstract: The adaptive gradient (AdaGrad) method is an optimization algorithm widely used in the field of artificial intelligence. An adaptive gradient stochastic parallel gradient descent (SPGD) algorithm (AdaSPGD algorithm), combining an AdaGrad algorithm with an SPGD algorithm, is innovatively introduced and implemented in coherent beam synthesis. The performance of a coherent beam combination system utilizing the AdaSPGD method is validated by numerical simulation of straightening static phase aberrations. The results of the simulations indicate that the AdaSPGD algorithm not only can effectively solve the trouble of difficulty in selecting the gain coefficient in the actual beam combining system, but also can accelerate the convergence of the phase control algorithm. Furthermore, the effectiveness of the proposed algorithm is demonstrated by means of the experimental investigation on coherent beam synthesis of a two-channel fiber array. The AdaSPGD algorithm is a satisfactory modification of the conventional SPGD algorithm.