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针对基于物理知识的多阶段分数神经网络的多精度计算
1School of Mathematics and Statistics, Gansu Key Laboratory of Applied Mathematics and Complex Systems, Lanzhou University, Lanzhou 730000, People's Republic of China.
Chaos (Woodbury, N.Y.)
|November 4, 2025
概括
多级分数物理信息神经网络 (fPINNs) 实现了亚扩散方程的高精度解决方案,精度提高到10-7∼10-8. 这种方法克服了非本地运营商在传统fPINNs中所面临的挑战.
科学领域:
- 计算数学 计算数学 计算数学
- 数字分析 数字分析
- 科学计算科学计算
背景情况:
- 分数物理信息的神经网络 (fPINNs) 是由Pang等人引入的. (2019) 对于亚扩散方程,实现相对误差为10-3∼10-4.
- 由于非本地运营商造成的规律性有限,对亚扩散模型的高精度数值解决方案具有挑战性.
研究的目的:
- 开发一种先进的数值方法,用于高精度解决子扩散方程.
- 解决现有的fPINNs在处理亚扩散模型的低规律性方面的局限性.
主要方法:
- 引入多级分数物理信息神经网络 (fPINNs).
- 在传统的多阶段PINN框架的基础上构建 (Wang & Lai, 2024).
- 在统一和非统一的计算网格上实施和测试.
主要成果:
- 实现了相对误差的显著改善,在亚扩散方程中达到10-7∼10-8.
- 证明了多阶段fPINNs在克服精度挑战方面的有效性.
- 验证了该方法在均和非均网格上的性能.
结论:
- 多阶段的fPINN为解决亚扩散方程提供了强大的,准确的方法.
- 与以前的fPINN技术相比,提出的方法提高了数值精度.
- 这一进步为模拟复杂的分数动态提供了更可靠的工具.
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