具有物理信息的概率性扩散场的生成性超弹性
Vahidullah Taç1, Manuel K Rausch2, Ilias Bilionis1
1Department of Mechanical Engineering, Purdue University, West Lafayette, IN, USA.
概括
本研究引入了一种新的方法,使用生成模型准确预测复杂的材料行为,并将不确定性和空间变化纳入增强的超弹性模型.
科学领域:
- 计算力学是计算力学.
- 材料科学是一种材料科学.
- 应用数学 应用数学 应用数学
背景情况:
- 自然材料具有复杂,非线性,异构和异质的机械性能.
- 数据驱动的应变能量函数可以模拟这些行为,但往往忽视不确定性和空间异质性.
- 现有的方法缺乏灵活性来捕捉物质反应的全部复杂性.
研究的目的:
- 开发一种基于数据的方法来建模超弹性材料,该方法包含不确定性和空间异质性.
- 为了创建灵活和受物理约束的应变能量功能.
- 推进对生物组织等复杂材料的预测建模.
主要方法:
- 利用神经常规方程 (NODE) 来构建多凸的应变能量函数.
- 采用概率扩散模型,从噪声中产生各种应变能量函数.
- 扩展了空间相关输出的扩散模型,以表示异构的材料特性.
主要成果:
- 成功生成了具有固有的不确定性可信的应变能量函数.
- 证明了对任意几何形状的空间异质材料特性进行建模的能力.
- 通过生物组织的合成和实验数据验证了该方法.
结论:
- 这种生成模型方法通过包括不确定性来显著增强数据驱动的超弹性模型.
- 该方法为预测复杂,异质材料的机械行为提供了一个强大的框架.
- 代表了计算材料科学和预测建模领域的重大进步.
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