一种基于梯度的机器学习启发的反向建模方法用于表征非线性组织特性
Hossein Geshani1, Iman Borazjani2
1J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, College Station, TX, 77840, USA.
Annals of biomedical engineering
|March 4, 2026
概括
这项研究引入了一种新的反向溶解器,从变形数据中确定软组织特性和纤维方向. 该方法有效地恢复复杂的材料行为,这对于了解临床应用中的组织力学至关重要.
科学领域:
- 生物力学 生物力学
- 计算力学是计算力学.
- 医学成像分析分析 医学成像分析
背景情况:
- 准确地描述软组织材料特性对于临床应用至关重要.
- 了解异型非线性行为和纤维方向是建模组织力学的关键.
研究的目的:
- 开发和验证基于梯度的反向溶解器,用于检索软组织中的异型非线性材料特性和纤维方向.
- 根据已知的变形和外部负荷进行表征,解决一个重要的临床目标.
主要方法:
- 一个基于梯度的反向溶解器直接最小化节点力余量,避免重复前进模拟.
- 使用多层感知子 (MLP) 将空间位置映射到行为,对材料属性进行参数化.
- 剩余光滑和并行雅可比计算增强了融合速度;支持一般的组成规律.
主要成果:
- 解决器成功地在复杂的3D几何形状中恢复了空间变化的弹性和异型纤维分布,在生物假体心脏上进行了演示.
- 仅剩余的配方提供了比物理信息神经网络 (PINN) 方法更快的优化.
- 虽然Fung模型显示参数非唯一性,但光纤方向恢复仍然强大.
结论:
- 开发的反向溶解器有效地恢复了非线性材料特性和纤维方向,用于复杂的3D软组织问题,包括那些有很大的变形的问题.
- 这种方法有望促进软组织力学的理解和临床应用.
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