数据驱动的连续损伤机制与内置的物理
Vahidullah Tac1, Ellen Kuhl2, Adrian Buganza Tepole1,3
1School of Mechanical Engineering, Purdue University, West Lafayette, Indiana, USA.
Extreme Mechanics Letters
|October 7, 2024
概括
这项研究将神经常规微分方程 (NODE) 扩展到软材料损伤模型. 新方法准确地捕捉了组织中的能量消耗和物质降解.
科学领域:
- 连续性力学是连续性的.
- 材料科学是一种材料科学.
- 计算力学是计算力学.
背景情况:
- 像和织物这样的软材料经历了很大的变形和损伤,影响了它们的功能.
- 连续损伤力学为理解能量消耗提供了一个热力学上一致的框架.
- 深度学习为复杂的材料行为提供了高精度,但在物理约束下建模不弹性仍然具有挑战性.
研究的目的:
- 扩展神经常规微分方程 (NODE) 用于模拟软材料中的能量消散.
- 将热力学上一致的框架纳入深度学习,用于材料建模.
- 为了应对模拟无弹性行为的挑战,在神经网络中内置物理.
主要方法:
- 使用具有不弹性潜力和单调收益率函数的神经普通微分方程 (NODE).
- 引入一种新的网络架构,能够模拟随意的超弹性材料,具有自动多凸性.
- 在各种损坏模型中展示网络架构的灵活性.
主要成果:
- 开发的NODE成功地以热力学一致的方式模拟能量消耗.
- 网络架构本质上满足了诸如多凸性之类的物理约束.
- NODE从合成数据中准确地重新发现损伤功能,并描述实验软组织数据.
结论:
- 神经普通微分方程 (NODE) 提供了一种强大而灵活的方法来建模复杂的材料行为,包括不弹性和损伤.
- 这种数据驱动的方法为理解软材料中的能量消耗提供了一个热力学上一致的框架.
- 该方法显示了对软组织的合成和实验数据进行表征的巨大潜力.
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