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可编程的次脉冲控制和LSTM-PINN预测在中红外SOPO中

Xiaodai Yao, Huaju Wang, Peilun Ni

|November 14, 2025

概括

研究人员开发了一种可编程脉冲爆发的中红外激光系统. 这一突破允许精确控制子脉冲特征,简化激光实验并实现智能控制.

科学领域:

激光物理激光物理
非线性光学是非线性光学.
材料科学材料科学材料科学

背景情况:

产生具有可控制子脉冲特征的中红外 (MIR) 脉冲爆发是激光技术的一个重大挑战.
现有的方法往往缺乏灵活性,可以独立调整脉冲数,间距和振幅.

研究的目的:

展示一种可编程MIR脉冲爆发生成的新型自光参数振荡器 (SOPO).
为了实现对子脉冲特征 (数量,间距,振幅) 的独立控制.
开发一个人工智能驱动的框架来预测激光参数并简化实验建模.

主要方法:

使用基于周期极化酸 (Nd:MgO:PPLN) 晶体的3.8微米SOPO.
采用动态阶段式Q开关来精确控制脉冲生成.
开发并应用了基于物理学的长短记忆物理信息神经网络 (LSTM-PINN) 框架.

主要成果:

实现了分脉冲数,间隔和振幅的独立控制.
在10kHz的重复频率下,输送的次脉冲能量为77.3μJ.
获得的最小次脉冲宽度为23.5 ns,间隔为150 ns.
LSTM-PINN框架准确预测了高R2值的脉冲宽度,振幅和能量 (训练时为0.99,验证时为0.93).

结论:

展示了一种灵活可编程的MIR脉冲爆发生成系统.
由人工智能驱动的预测框架大大简化了激光实验和建模.
这项工作为智能激光控制和中红外光谱的先进应用铺平了道路.

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