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基于物理学的神经网络用于血流模型中的参数估计

Jeremías Garay¹, Jocelyn Dunstan², Sergio Uribe³

¹Department of Mechanical and Metallurgical Engineering, Pontificia Universidad Católica de Chile, Chile; Center of Biomedical Imaging, Pontificia Universidad Católica de Chile, Chile; Millennium Institute for Intelligent Healthcare Engineering (iHealth), Chile.

Computers in biology and medicine

|June 16, 2024

概括

基于物理学的神经网络 (PINNs) 从有限的血液动力学数据有效地估计参数和速度场. 这种深度学习方法对复杂的物理系统模拟非常有希望,其性能优于具有更多参数的传统方法.

科学领域:

计算流体动力学的流体动力学.
生物医学工程生物医学工程
机器学习机器学习

背景情况:

基于物理学的神经网络 (PINNs) 对具有不完整数据的反向问题非常有价值.
血液动力学由于难以进行边界建模和稀缺的高质量测量提出了挑战.

研究的目的:

应用PINNs来估计大动脉中减少顺序模型参数和速度场.
通过噪音散射测量,分析静态和瞬态流量模式中的性能.

主要方法:

利用PINNs的方法来估计参数和重建速度场.
研究了两个不同的流动模式:静态和瞬态.
将PINNs的性能与卡尔曼波器方法进行比较.

主要成果:

从模拟数据中获得了强大而准确的参数估计.
速度重建的准确性取决于测量质量和流的复杂性.
在估计更高数量的参数时,PINNs的表现优于卡尔曼过器.

结论:

PINNs提供了一种强大的深度学习驱动的方法来模拟复杂的合物理系统.
该方法证明了在推进血液动力学建模和分析方面具有重大潜力.

关键词:

血液的流动血液的流动血液动力学血液动力学患者特定的模型基于物理学的神经网络.减少的订单建模减少的订单建模

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