arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2108.09869physics.optics
收藏

超快光纤激光器中智能呼吸孤子的产生

Intelligent breathing soliton generation in ultrafast fibre lasers

Xiuqi Wu, Junsong Peng, Sonia Boscolo, Ying Zhang, Christophe Finot, Heping Zeng

摘要:

利用超快激光器产生脉冲是一项具有挑战性的任务,因为达到特定的锁模状态通常涉及调整多个控制参数,以及广泛的可访问脉冲动态。机器学习工具最近显示出设计智能激光器的前景,这些激光器可以将自己调整到所需的操作状态。然而,机器学习算法主要是针对参数不变的稳态脉冲生成机制而设计的,而激光器中不断变化的脉冲模式的智能激励在很大程度上仍未被探索。呼吸孤子表现出周期性的振荡行为,出现在超快光纤激光器的普遍锁模制度,吸引了相当大的兴趣,凭借其与一系列重要的非线性动力学,如例外点,和费米-帕斯塔-乌拉姆悖论。在这里,我们实现了一个进化算法的呼吸制度的自我优化的光纤激光器锁模通过四参数非线性偏振演化。取决于用于优化过程的优值函数的规格,得到各种呼吸孤子状态,包括具有可控振荡周期和呼吸比的单呼吸子,和具有可控数量的基本成分的呼吸分子复合物。我们的工作为探索和优化非线性系统中的复杂动力学开辟了一条新的途径。

英文摘要:

Harnessing pulse generation from an ultrafast laser is a challenging task as reaching a specific mode-locked regime generally involves adjusting multiple control parameters, in connection with a wide range of accessible pulse dynamics. Machine-learning tools have recently shown promising for the design of smart lasers that can tune themselves to desired operating states. Yet, machine-learning algorithms are mainly designed to target regimes of parameter-invariant, stationary pulse generation, while the intelligent excitation of evolving pulse patterns in a laser remains largely unexplored. Breathing solitons exhibiting periodic oscillatory behavior, emerging as ubiquitous mode-locked regime of ultrafast fibre lasers, are attracting considerable interest by virtue of their connection with a range of important nonlinear dynamics, such as exceptional points, and the Fermi-Pasta-Ulam paradox. Here, we implement an evolutionary algorithm for the self-optimisation of the breather regime in a fibre laser mode-locked through a four-parameter nonlinear polarisation evolution. Depending on the specifications of the merit function used for the optimisation procedure, various breathing-soliton states are obtained, including single breathers with controllable oscillation period and breathing ratio, and breather molecular complexes with a controllable number of elementary constituents. Our work opens up a novel avenue for exploration and optimisation of complex dynamics in nonlinear systems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑