基于物理信息的神经网络的时间分数微分方程反向框架,用于风病学
Sukirt Thakur1, Harsa Mitra1, Arezoo M Ardekani1
1School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Biology
|July 29, 2025
概括
基于物理学的神经网络 (PINNs) 现在通过时间分数导数来解决反向问题. 这种数据效率高的方法准确地模拟复杂的系统,如异常扩散和粘弹性,即使有噪音数据.
科学领域:
- * 计算数学和物理.
- * 复杂动态系统的建模.
背景情况:
- * 时间分数微分方程模拟生物传输和粘性弹性中的依赖记忆的动态.
- *用这些方程解决反向问题是具有挑战性的,因为稳定性,独特性和数据限制.
- *现有的物理信息神经网络 (PINNs) 通常仅限于整数顺序的导数.
研究的目的:
- * 开发一个PINN框架,用于时间分数导数的反向问题.
- *将框架应用于异常扩散和分数粘弹性.
- *从合成和实验数据中推断关键物理参数.
主要方法:
- * 开发了一个定制的PINN框架,用于时间分数反向问题.
- * 嵌入了缩放的残余损失函数,以提高对噪声的强度.
- *使用合成数据集和来自猪组织的实验数据验证了该方法.
主要成果:
- * 在扩散模型中成功推断了一般化扩散系数和分数导数顺序.
- * 在分数麦克斯韦尔模型中准确地恢复了放松参数.
- *即使在25%的高斯噪声下,也实现了参数恢复的10%以下的相对误差.
- * 在猪组织实验中证明了放松模块的准确预测.
结论:
- *开发的PINN框架有效地从杂和稀疏的数据中学习分数动态.
- * 这种方法对模拟复杂的生物和机械系统具有显著的前景.
- * 这项工作将PINNs的适用性扩展到更广泛的分数微分方程类别.
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