使用基于物理学的神经网络,确定复杂的超弹性材料的构成参数
1School of Engineering, Brown University, Providence, RI 02912, USA. hanxun_jin@alumni.brown.edu.
Soft matter
|July 2, 2024
概括
这项研究引入了物理信息神经网络 (PINN) 框架,以准确识别复杂软材料中的材料参数. 强大的模型即使在杂的实验数据和复杂的几何形状下也能工作.
科学领域:
- 计算力学是计算力学.
- 材料科学是一种材料科学.
- 机器学习 机器学习
背景情况:
- 在复杂材料中确定构成性参数是具有挑战性的.
- 现有的物理信息神经网络 (PINNs) 具有复杂的行为和实验数据的局限性.
- 具有复杂几何形状的软材料需要先进的建模技术.
研究的目的:
- 开发一个基于PINN的强大框架,用于识别软材料中的材料参数.
- 解决当前PINN框架对复杂的宪法法和大变形的局限性.
- 为了使准确的参数识别使用多模式合成实验数据.
主要方法:
- 开发了一种新的PINN框架,用于软材料中的参数识别.
- 用于训练的多模式合成数据集,包括全场变形和负载历史.
- 在平面应力条件下训练PINN模型以确定无压缩Arruda-Boyce模型的参数.
- 确保了算法对实验噪声的稳定性.
主要成果:
- 该PINN框架准确地确定了不可压缩的Arruda-Boyce模型的组成参数.
- 即使在5%的实验噪声下,也实现了低于5%的识别错误.
- 对于具有复杂几何形状和复杂构成性行为的样品,证明了强度.
- 成功处理大变形和平面应力条件.
结论:
- 拟议的PINN框架为复杂固体中模量识别提供了一个强大的方法.
- 这种方法对于具有几何和构成复杂性的材料特别有效.
- 该框架促进了PINNs在材料科学和工程中的应用.
- 从实验数据中实现更准确的材料表征.
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