以物理为基础的数据驱动的构成模型的发现,适用于应变率敏感的软材料
Kshitiz Upadhyay1, Jan N Fuhg2, Nikolaos Bouklas2,3
1Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803 USA.
概括
一个新的基于物理的机器学习模型通过将连续热力学与软材料的高斯过程回归相结合,准确地预测材料的行为.
科学领域:
- * 计算力学 计算力学
- * 材料科学 材料科学
- * 机器学习 * 机器学习
背景情况:
- * 开发精确的构成模型,以应变率敏感的软材料仍然是一个挑战.
- *传统模型难以捕捉复杂的行为,需要大量的实验数据.
- * 将物理原理与数据驱动方法相结合,为改进建模提供了一个有希望的途径.
研究的目的:
- * 提出一种新的数据驱动的构成模型方法,将连续热力学和机器学习结合起来.
- * 为了证明该模型在应变率敏感的软材料上的有效性.
- * 在机器学习框架内强制执行基于物理的约束.
主要方法:
- *基于粘性消散的粘性超弹性框架与应力分解.
- * 应力组件表示的不可缩小的完整性基础.
- *高斯过程回归替代模型训练了应变和应变速率不变的变量.
主要成果:
- * 基于物理学的数据驱动模型准确地捕捉了应力-应变-应变速率反应.
- * 在多种变形模式中实现了更好的预测准确性和概括性.
- * 该模型证明了与有限的数据集的兼容性.
结论:
- * 拟议的方法成功地将基于物理学的约束集成到数据驱动的构成模型中.
- * 这种方法提供了比经典和纯数据驱动模型更强大和更可通用的替代方案.
- * 该框架显示了模拟复杂物质行为,减少数据需求的巨大潜力.
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