分数次方程神经网络 (fSENNs) 方法,用于精确解决时空分数部分微分方程的方法
Jiawei Wang1,2, Yanqin Liu2, Limei Yan2
1School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.
Chaos (Woodbury, N.Y.)
|April 8, 2025
概括
分数次方程神经网络 (fSENNs) 提供了一种新的方法,用于找到空间-时间分数部分微分方程 (fPDEs) 的精确解决方案. 这种方法将分数里卡蒂方程解决方案集成到神经网络中,为复杂的动态产生新的分析结果.
科学领域:
- 应用数学 应用数学 应用数学
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 对时空分数局部微分方程 (fPDEs) 的分析解决方案对于建模复杂系统至关重要.
- 现有的方法可能无法捕捉到fPDEs固有的全部动态行为.
研究的目的:
- 引入一种新的方法,分数子方程神经网络 (fSENNs),用于构建时空fPDEs的精确解决方案.
- 为了证明fSENNs在产生各种类型的分数溶液中的有效性.
主要方法:
- 通过将分数里卡蒂方程解决方案嵌入到神经网络架构中,提出了分数子方程神经网络 (fSENNs).
- 利用fSENNs来推导试验函数来解决时空fPDEs.
- 将该方法应用于分数电报,费舍尔和CKdV-mKdV方程.
主要成果:
- 成功获得了概括的分数超标函数解,三角函数解和理性解.
- 证明了fSENNs能够产生与现有方法相比新的和更多的解决方案的能力.
- 视觉化了与波相关的解决方案的动态特征.
结论:
- fSENN为解决时空fPDEs提供了一个强大而多功能框架.
- 在神经网络中整合小数次方程为小数微积分的分析解决方案开辟了新的途径.
- 该方法为各种分数微分方程的复杂动态提供了宝贵的见解.
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