超快速且准确的非线性足形变形使用图形神经网络进行预测
Taehyeon Kang1, Jiho Kim2, Hyobi Lee2
1Department of Mechanical and Biomedical Engineering, Ewha Womans University, Seoul, 03760, Republic of Korea; Department of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, 03760, Republic of Korea.
Journal of the mechanical behavior of biomedical materials
|December 13, 2024
概括
图形神经网络 (GNN) 提供了一个更快,更便宜的方式来设计定制的脚. 这种人工智能方法可以准确地预测脚的形状,改善脚部疾病的非手术治疗方法.
科学领域:
- 生物力学 生物力学
- 计算建模计算建模
- 人工智能的人工智能是人工智能.
背景情况:
- 脚部疾病的患病率越来越高,需要有效的非手术治疗.
- 定制的内提供了一个有希望的解决方案,但传统的设计是缓慢和昂贵的.
- 有限元分析 (FEA) 是计算密集型的用于预测脚的变形.
研究的目的:
- 探索使用图形神经网络 (GNN) 来预测负载下的3D脚形状.
- 根据数据集大小评估GNN性能.
- 为了评估GNN的速度和准确性,与FEA相比,用于内设计.
主要方法:
- 使用MeshGraphNet框架用于GNN开发.
- 训练有素的GNN,具有186个3D脚体几何和FEA预测的变形.
- 优化了GNN权重,以准确预测脚位移.
主要成果:
- 在预测脚部位移方面,GNN模型实现了超过95%的准确性 (R2值).
- GNN的速度大约是传统FEA模拟的97.52倍.
- 在不同大小的数据集中测试了性能.
结论:
- GNN显著提高了效率,并降低了定制内制造的成本.
- 这种人工智能驱动的方法代表了非手术足部疾病治疗的重大进步.
- GNN显示出革命定制整形设计的巨大潜力.
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