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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 Gebauer, Martin R Pfaller, Jason M Szafron

|May 3, 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,使得心血管研究的大器官规模模拟成为可能.

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
软组织建模模型

背景情况:

约束混合理论对于软组织生长和重塑 (G&R) 的建模至关重要.
现有的计算模型面临高成本,限制了大型器官规模的模拟.
长期的G&R模拟在计算上特别密集.

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.
在G&R建模中降低计算成本和内存消耗.

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历史数据随时间推移的影响.
在组织贴片和双心室心脏模型上验证了适应模型.

主要成果:

适应性集成策略显著降低了计算成本和内存使用量.
在双心室心脏模型中,模拟被加快了三倍,记忆需求减少到六分之一.
在较长的模拟期内,计算成本的降低变得更加明显.

结论:

历史变量的自适应集成使得大器官规模的G&R模拟成为可能.
这些策略大大降低了计算成本,允许更精细的模型和更长的模拟持续时间.
开发的方法对于推进心血管G&R研究至关重要.

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