Reversely Exploring Higher-Order Effects in a Fiber Laser through Physics-Informed Recursive Neural Network,ACS Photonics

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Reversely Exploring Higher-Order Effects in a Fiber Laser through Physics-Informed Recursive Neural Network
ACS Photonics ( IF 6.5 ) Pub Date : 2024-09-06 , DOI: 10.1021/acsphotonics.4c01235
Jingxuan Sun ₁ , Yiqing Shu ₁ , Yanqi Ge ₂ , Jianqing Li ₃ , Weicheng Chen ₁

Affiliation

In ultrafast fiber lasers, the influence of higher-order effects on mode-locked pulses becomes increasingly notable as the pulse width decreases from the subpicosecond to the femtosecond level. However, accurately predicting the underlying higher-order effects in nonlinear nonconservative systems remains difficult with forward-calculating nonlinear partial differential equations. We propose a novel physics-informed recursive neural network (PIRNN) model to reversely explore the underlying higher-order effects in fiber lasers. Based on experimental data, the PIRNN can reversely deduce the coefficients of the group velocity dispersion, third- and fourth-order dispersion effects, and the third- and fifth-order nonlinearities in fibers to establish higher-order nonlinear Schrödinger equations and Ginzburg–Landau equations, which govern the theoretical model of a fiber laser. The PIRNN further demonstrates which higher-order effects are dynamically stimulated for different cases of 879 fs and 1.62 ps solitons in the experiments. Furthermore, the PIRNN-predicted spectrotemporal and phase information is verified by both experimental results and forward-calculating results of the theoretical model. Additionally, the physical rule of electromagnetic resonant radiation of bound electrons in SiO₂-based fibers is reversely deduced by constructing our self-designed dielectric neural network. Our novel approach for reversely exploring underlying higher-order effects introduces a novel perspective for investigating nonconservative systems.

中文翻译：

通过物理信息递归神经网络反向探索光纤激光器中的高阶效应

在超快光纤激光器中，随着脉冲宽度从亚皮秒级减小到飞秒级，高阶效应对锁模脉冲的影响变得越来越明显。然而，使用正向计算非线性偏微分方程，准确预测非线性非保守系统中潜在的高阶效应仍然很困难。我们提出了一种新的物理信息递归神经网络（PIRNN）模型，以反向探索光纤激光器中潜在的高阶效应。基于实验数据，PIRNN 可以反向推导出光纤中群速度色散、三阶和四阶色散效应以及三阶和五阶非线性的系数，以建立高阶非线性薛定谔方程和 Ginzburg-Landau 方程，这些方程支配光纤激光器的理论模型。PIRNN 进一步证明了在实验中，对于 879 fs 和 1.62 ps 孤子的不同情况，哪些高阶效应是动态激发的。此外，PIRNN 预测的光谱时间和相位信息通过实验结果和理论模型的正演计算结果进行了验证。此外，通过构建我们自己设计的介电神经网络，反向推导了 SiO₂ 基纤维中束缚电子的电磁谐振辐射的物理规律。我们反向探索潜在高阶效应的新方法为研究非保守系统引入了一种新的视角。

更新日期：2024-09-06

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