Coherent beam combining experiments based on stochastic parallel gradient descent algorithm
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摘要: 介绍了随机并行梯度下降算法的基本原理，对算法流程进行了仿真验证，并对其中随机扰动幅度和增益系数两个关键参数进行了仿真分析。分析结果表明，这两个参数存在一个最适区间，只有在此区间内取值时算法才能有效收敛。以仿真分析为依据开展了光纤激光的相干合成实验，结果表明光束相干合成效果显著，有效地验证了仿真分析的结果。
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关键词:
- 随机并行梯度下降算法 /
- 光纤激光 /
- 相干合成 /
- 高功率激光
Abstract: The principle of stochastic parallel gradient descent (SPGD) algorithm is introduced, and the algorithm flow is verified through simulation. Two critical factors, the stochastic perturbation and the gain coefficient, are especially analyzed. The simulation results show that there is a most appropriate interval for selecting the two factors. Only with the two factors selected in this interval, the algorithm can achieve the best convergence value. Based on the simulation results, the coherent beam combining experiments are carried out with fiber lasers, resulting in significant effect of beam combining. The experimental results prove the results of simulation above. In conclusion, the research results would improve the design of coherent beam combining experiments for high power laser in the future. -
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