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对于可整的非局部方程,PT-对称PINN:前向和反向问题
Wei-Qi Peng1, Yong Chen1,2
1School of Mathematical Sciences, Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai 200241, People's Republic of China.
Chaos (Woodbury, N.Y.)
|April 5, 2024
概括
一种新方法,PT-对称半监督神经网络 (PTS-PINN),有效地解决PT-对称非局部方程. 这种方法通过将非本地术语视为本地组件并将物理信息嵌入到损失函数中来提高准确性.
科学领域:
- 计算物理 计算物理
- 应用数学 应用数学 应用数学
- 非线性动力学是一种非线性动力学.
背景情况:
- 解决PT对称非局部方程对传统的物理信息神经网络 (PINN) 构成挑战.
- 这些方程中的非局部项使神经网络架构中的直接数值差异化变得复杂.
研究的目的:
- 引入一种新的方法,PT-对称的半监督神经网络 (PTS-PINN),用于解决PT-对称的非局部方程.
- 为复杂的非线性系统增强基于神经网络的解决方案的准确性和适用性.
主要方法:
- PTS-PINN将非本地术语重新定义为合的本地组件,避免直接区分.
- PT对称性的物理信息被整合到神经网络的损失函数中,以提高准确性.
- 该方法在各种非局部方程上进行了测试,包括NLS和三波相互作用系统.
主要成果:
- 在解决各种PT-对称非局部方程的前向和反向问题方面,PTS-PINN表现出强的性能.
- 该方法在学习由非局部动态控制的大型时空尺度流波方面表现出了卓越的能力.
- 数字实验证实了PTS-PINN比标准方法的有效性和更高的准确性.
结论:
- PTS-PINN为解决PT对称非局部方程提供了一个强大而准确的框架.
- 将物理对称信息集成到损失函数中是该方法成功的一个关键因素.
- 这种方法为模拟非线性物理学和应用数学中的复杂现象开辟了新的途径.
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