一个以物理为导向的机器学习模型,用于预测固体在大变形时的粘弹性
1Research Institute of Interdisciplinary Science, School of Materials Science and Engineering, Dongguan University of Technology, Dongguan 523808, China.
Polymers
|November 27, 2024
概括
物理引导的机器学习模型现在可以使用有限的数据预测固体粘弹性. 一种新的循环神经网络方法整合了时间,拉伸和物理定律,用于准确的建模.
科学领域:
- 固体机械学 固体机械学
- 计算材料科学 计算材料科学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 物理引导机器学习 (PGML) 通过整合数据和物理定律,有效地建模物质构成关系.
- 现有的PGML方法在弹性和可塑性等时间独立的行为方面表现出色,但在时间依赖的粘性弹性方面扎.
- 精确的粘性弹性建模具有挑战性,特别是由于其路径和时间依赖性,实验数据有限.
研究的目的:
- 开发一种新的以物理学为指导的循环神经网络 (RNN) 模型,用于预测在大型变形下固体的粘弹性行为.
- 通过利用物理引导的初始化来解决数据稀缺性在模拟粘性弹性的挑战.
- 为了能够准确地预测应力,考虑到时间和负载路径的依赖性.
主要方法:
- 开发了一种混合GRU-FNN (关闭循环单元前神经网络) 模型,接受时间和拉伸/拉伸序列作为输入.
- 采用物理引导的初始化策略,使用VHB聚合物的通用麦克斯韦尔模型的数值数据.
- 该模型是使用有限的实验数据进行训练的,从物理信息初始化中受益,以克服数据稀缺.
主要成果:
- 提出的PGML模型成功地预测了固体在大变形时的时间和路径依赖的粘弹性行为.
- 物理引导的初始化显著改善了模型性能,特别是在实验数据有限的场景中.
- 该GRU-FNN架构有效地捕获了粘性弹性材料固有的复杂的应力-张力-时间关系.
结论:
- 物理指导的循环神经网络为复杂的粘弹性现象建模提供了强大的框架,即使实验数据稀缺.
- 通过初始化整合物理知识,提高了材料科学机器学习模型的稳定性和准确性.
- 这种方法提高了对粘性弹性材料构成关系的预测能力,这对材料设计和性能模拟有影响.
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