一种转移学习方法来解决基于等效线性化的福克-普朗克方程
Gege Wang1, Xiaolong Wang1,2, Qi Liu3
1School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710072, China.
Chaos (Woodbury, N.Y.)
|August 8, 2025
概括
本研究介绍了一种新的转移学习方法,以有效地解决随机系统的福克-普朗克 (FP) 方程. 这种方法加快了计算速度,并保持了复杂系统的准确性.
科学领域:
- 计算物理 计算物理
- 应用数学 应用数学 应用数学
- 机器学习 机器学习
背景情况:
- 解决福克-普朗克 (FP) 方程对于分析随机系统至关重要.
- 当前的方法可能是计算密集型,限制了它们的应用.
- 需要更高效,更准确的解决方案技术.
研究的目的:
- 开发一种基于转移学习的有效方法来解决福克-普朗克方程.
- 为了加速解决复杂的随机系统的培训过程.
- 证明方法的准确性和概括能力.
主要方法:
- 相当的线性化来统一随机微分方程.
- 一个预训练的神经网络框架,灵感来自转移学习.
- 用高斯和莱维噪声对一维和二维系统进行数值实验.
主要成果:
- 拟议的转移学习方法显著减少了解决FP方程的培训时间.
- 该方法准确地学习FP方程的轮.
- 对于高斯和莱维噪声系统有效.
结论:
- 转移学习方法为福克-普朗克方程提供了一个计算效率高的解决方案.
- 该方法在不同的随机系统中表现出强大的概括能力.
- 这种技术通过提高计算效率来增强对随机系统的研究.
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