使用构成性循环神经网络对肌肉的自动化模型发现
Lucy M Wang1, Kevin Linka1, Ellen Kuhl1
1Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, United States.
概括
这项研究引入了一种新的机器学习方法,将前和反复的神经网络结合起来,以建模软生物组织的复杂粘弹性行为,并从实验数据中准确地发现构成模型和参数.
科学领域:
- 计算力学是计算力学.
- 生物材料科学是生物材料的科学.
- 在工程领域的机器学习.
背景情况:
- 软生物组织表现出复杂的机械行为,硬度取决于变形和变形速度.
- 传统的建模包括选择和将时间依赖的构成模型的参数与实验数据相匹配.
- 现有的机器学习方法,如前神经网络,在模拟超弹性方面表现出色,但与依赖历史的粘性弹性的性质斗争.
研究的目的:
- 开发一种新的机器学习框架,能够同时发现粘弹性软组织的构成模型和参数.
- 整合超弹性和粘弹性建模使用混合神经网络架构,灵感来自于准线性粘弹性.
- 用被动骨肌的实验数据与既定方法对模型的性能进行验证.
主要方法:
- 开发了一种混合神经网络架构,将高弹性响应的前网络和粘性响应的循环神经网络结合起来.
- 该网络使用被动骨肌肉的无限制压缩放松实验数据进行训练.
- 发现的模型与Neo-Hookean标准线性固体模型,基于先进力学模型和香草循环神经网络进行了比较.
主要成果:
- 新的构成性循环神经网络成功发现了Mooney-Rivlin类型的能量函数和Prony系列类型的放松函数.
- 与新霍金标准线性固体和香草循环神经网络相比,发现的模型在未见的数据上显示出更高的预测准确性.
- 该模型满足了基本的物理原理,并且即使使用有限的实验数据,也能很好地概括.
结论:
- 构造性循环神经网络提供了一种强大的数据驱动方法,可以自主发现软粘弹性组织的准确模型和参数.
- 这种reologically-informed网络架构有效地捕捉了时间独立和时间依赖的机械行为.
- 开发的框架推进了生物组织机制的建模,为传统方法提供了强大的替代方案.
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