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适应性整合历史变量在受限制的混合模型中,用于器官规模的生长和重塑

Amadeus M Gebauer¹, Martin R Pfaller², Jason M Szafron³

¹Institute for Computational Mechanics, Department of Engineering Physics & Computation, TUM School of Engineering and Design, Technical University of Munich, Garching, Germany.

International journal for numerical methods in biomedical engineering

|September 20, 2024

概括

软组织生长和重塑 (G&R) 的计算模型通过适应性策略得到增强. 这些方法显著降低了计算成本,使大器官规模的G&R模拟成为可能.

科学领域:

计算力学是计算力学.
生物物理学的生物物理.
组织工程是组织工程.

背景情况:

计算模型对于预测软组织生长和重塑 (G&R) 是至关重要的.
约束混合理论在心血管G&R模型中被广泛使用.
高的计算成本限制了大型器官规模的模拟.

研究的目的:

开发适应性策略,将历史变量集成到受约束混合模型中.
为了实现软组织G&R的计算效率高,大器官规模的模拟.
为了减少长距离模拟中的内存消耗和计算时间.

主要方法:

提出了在受约束混合模型中适应性整合历史变量的两个策略.
利用组织降解来减少随着时间的推移而沉积的组织的影响.
分析了组织贴片上的模型性能和模拟的双心室心脏模型.

主要成果:

适应性策略显著降低了计算成本和内存需求.
在双心室心脏模型中,可以对高血压状况进行模拟.
实现了三倍的速度提升和六倍的内存使用量减少.

结论:

历史变量的自适应集成大大降低了G&R模拟的计算成本.
允许更精细的分辨率模型和更长的模拟时间.
促进了大型器官规模的模拟,这对于理解心血管疾病至关重要.

关键词:

计算建模计算建模有限制的混合物模型模型.增长和改造的增长和改造.机械生物学机械生物学器官尺度上的器官尺度

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