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相关概念视频

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

84
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
84

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Updated: Jul 11, 2025

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
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通过缩小顺序建模与基于深度学习的操作员近似,有效地近似心脏力学.

Ludovica Cicci1, Stefania Fresca1, Andrea Manzoni1

  • 1MOX-Dipartimento di Matematica, Politecnico di Milano, Milan, Italy.

International journal for numerical methods in biomedical engineering
|November 3, 2023
PubMed
概括

一种新的Deep-HyROMnet技术显著加快了针对患者的心脏力学模拟. 该方法使用缩小基础和深度学习来实现临床应用的快速,准确的结果.

关键词:
在POD-Galerkin减少订单模型.心脏机械学心脏机械学深度神经网络是一个神经网络.超缩减技术的超缩减技术运营商的近似值.参数化的微分问题 参数化的微分问题减少订单建模减少订单建模

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相关实验视频

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科学领域:

  • 计算力学是计算力学.
  • 生物医学工程 生物医学工程
  • 医疗保健中的人工智能

背景情况:

  • 高保真心力学模拟 (全序模型) 在计算上昂贵,限制了临床使用.
  • 精确的模拟需要精细的时空离散,甚至只需要几次心跳就需要数小时的计算时间.
  • 患者特异性心脏模型需要参数校准以进行虚拟场景探索,进一步增加计算需求.

研究的目的:

  • 开发一种计算效率高的方法,用于针对患者的心脏力学模拟.
  • 为了减少临床翻译的全顺序模型 (FOM) 的计算时间.
  • 为了实现先进的分析,如心脏力学中的不确定性量化.

主要方法:

  • 一种结合深度神经网络进行操作员近似的简化基础方法 (Deep-HyROMnet).
  • 基于投影的正确直角分解-加勒金方法与深度学习相结合.
  • 将一个3D心脏组织力学模型与一个0D血液循环模型以及一个依赖参数的替代品用于产生活性力.

主要成果:

  • 与传统的基于投影的减少顺序模型 (ROM) 相比,Deep-HyROMnet实现了数量级的计算速度提升.
  • 该方法为患者特定的心脏机制提供了非常准确的近似值,包括完整的心脏周期.
  • 准确的压力-体积循环被复制为生理和病理病例.
  • 预期不确定性量化分析,以前无法使用FOM,变得可行.

结论:

  • 深度HyROMnet技术在计算心脏力学方面取得了重大进展.
  • 这种方法加速了针对患者的模拟,为临床整合铺平了道路.
  • 能够进行复杂的分析,例如不确定性量化,以更好地了解心脏功能和疾病.