通过LrD-PINN在自我失焦的和非线性施罗丁格方程中发现PT对称的单子和参数
Bo-Wei Zhu1, Wen-Bo Bo1, Qi-Hao Cao1
1College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Lin'an 311300, China.
Chaos (Woodbury, N.Y.)
|July 17, 2023
概括
我们引入了一个学习速率衰减物理信息神经网络 (LrD-PINN),以准确预测非线性光学中的单子动态,显著扩展PT对称潜力的进化距离.
科学领域:
- 非线性光学是非线性光学.
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 非线性施罗丁格方程 (NLSE) 是描述各种物理系统中的波传播的基础.
- 在PT对称潜力中的单子动态对预测和分析提出了独特的挑战.
- 准确预测长期单体进化对于理解非线性光学现象至关重要.
研究的目的:
- 开发一个新的物理信息神经网络 (PINN) 具有学习速度衰减策略 (LrD-PINN).
- 预测对称,不对称和反对称单子在具有PT对称潜力的自我失焦和NLSE中的动态.
- 与现有方法相比,将预测的进化距离提高一个数量级.
主要方法:
- 为非线性光学动力学量身定制的LrD-PINN模型的实施.
- 训练和验证使用对称,不对称和反对称单子溶液的数据集.
- 探索学习速度衰减策略的好处,网络优化和反干扰能力.
- 从单离子溶液数据中重建非线性和潜在强度系数.
主要成果:
- LrD-PINN成功地预测了各种单体类型 (对称,不对称,反对称) 的单体动态.
- 在预测的进化距离 (一个数量级) 中实现了显著的提升.
- 该模型展示了强大的抗干扰性能和有效的系数重建.
结论:
- LrD-PINN为研究非线性光学中复杂的单子动态提供了强大而高效的工具.
- 机器学习的整合,特别是与学习速度衰减的PINNs,推进了光学单元研究领域.
- 这种方法为非线性光学系统提供了更丰富的见解和更强大的预测能力.
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