捕捉骨行为的多尺度性质:古典,数据驱动和混合技术
Melika Mohammadkhah1,2, Ardeshir Savari3, Sandra Klinge4
1Chair of Structural Mechanics and Analysis, Institute of Mechanics, Technische Universität Berlin, Straße des 17. Juni 135, 10623, Berlin, Germany. melika.mohammadkhah@tu-berlin.de.
Annals of biomedical engineering
|March 2, 2026
概括
本综述探讨了多尺度骨生物力学建模,比较了古典和数据驱动的计算框架. 它为选择准确捕捉骨的技术提供了指导.
科学领域:
- 生物力学 生物力学
- 计算生物学 计算生物学
- 材料科学 材料科学 材料科学
背景情况:
- 骨架构在多个时空尺度上表现出复杂的等级组织.
- 准确地捕捉骨的机械行为需要先进的计算建模技术.
- 现有方法在整合生物数据和平衡效率与可解释性方面面临挑战.
研究的目的:
- 通过使用经典和数据驱动的计算框架,审查多尺度骨建模的最新趋势.
- 评估这些方法的多功能性,可扩展性和生物数据集成能力.
- 根据应用上下文,为选择适当的建模技术提供可操作的指导.
主要方法:
- 对古典计算框架进行多尺度骨建模的比较评估.
- 评估数据驱动的框架,以捕捉骨的等级机械行为.
- 分析混合技术及其协同作用,以实现强大的和可通用的建模.
主要成果:
- 经典和数据驱动的框架在多尺度骨建模中都有明显的优势和局限性.
- 混合技术在生物力学建模中增强了稳定性和通用性的承诺.
- 建议使用层次决策矩阵来指导基于数据可用性和目标的技术选择.
结论:
- 选择合适的多尺度建模技术对于准确的骨生物力学至关重要.
- 混合方法代表了骨架构分析的有希望的未来方向.
- 本综述是促进多尺度骨生物力学研究的实际参考.
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