一个神经网络有限元三叶式心脏门模型,包含多体接触
Kenneth Meyer1, Christian Goodbrake1, Michael S Sacks1
1James T. Willerson Center for Cardiovascular Modeling and Simulation, Oden Institute for Computational Engineering and Sciences, Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, USA.
概括
一种新的神经网络有限元 (NNFE) 方法显著加速了心血管模拟. 这种方法使得快速的,针对患者的心脏门功能建模,改善临床护理的潜力.
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
- 机器学习应用程序 机器学习应用程序
背景情况:
- 针对患者的特定计算建模对于心血管疾病的治疗至关重要.
- 目前的有限元法 (FEM) 模拟对于临床使用来说太慢了.
- 为了确定最佳的治疗方法,需要加速模拟.
研究的目的:
- 开发和验证神经网络有限元 (NNFE) 方法,用于快速软组织器官模拟.
- 扩展NNFE以模拟三叶式心脏关闭,包括多体接触.
- 实现患者特定的计算建模,以改善心血管疾病治疗.
主要方法:
- 使用NNFE与传统的FEM网格和基于GPU的软件进行超弹性PDEs.
- 扩展NNFE以模拟多体接触的3D实体心脏门传单.
- 经过验证的NNFE与tIGAr用于单个小册子的关闭,分析节点位移错误和原纤维效应.
主要成果:
- 与FEM相比,NNFE实现了约100倍的加快速度 (0.28秒对单个小册子关闭的61秒).
- 与接触的全三叶式模拟需要5秒 (与FEM的小时相比).
- 平均节点位移误差为0.020mm (0.47%),具有生理上准确的变形模式.
结论:
- NNFE成功地模拟了复杂的3D软器官系统,如三叶式心脏门,具有很大的变形和多体接触.
- 该方法提供了快速的训练后模拟,对患者特定的预测模型有希望.
- 在临床应用中,NNFE在计算力学中推进机器学习.
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