使用神经常规微分方程的数据驱动的异型有限粘性弹性
Vahidullah Taç1, Manuel Rausch2, Francisco Sahli Costabal3
1Department of Mechanical Engineering, Purdue University, West Lafayette, IN, USA.
概括
我们使用神经常规微分方程开发了一个以数据为导向的模型,用于异型有限粘性弹性. 这种灵活的方法准确地模拟复杂的材料行为,优于传统方法.
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:
- 粘弹性描述了表现出时间依赖性应变的材料.
- 传统模型经常与复杂的,异型的行为和大变形作斗争.
- 基于物理学的约束,如客观性和热力学,对于准确的建模至关重要.
研究的目的:
- 开发一个完全数据驱动的模型,用于异型有限的粘性弹性.
- 将基于物理的约束纳入数据驱动的潜力.
- 为了能够在任意条件下准确地建模粘弹性材料.
主要方法:
- 使用神经常规微分方程 (NODE) 作为核心组件.
- 用数据驱动的功能取代传统的赫尔姆霍尔茨自由能量和消散潜力.
- 训练模型对来自各种生物和合成材料的应力应变数据进行训练.
主要成果:
- 数据驱动的模型成功地捕获了异构的有限粘性弹性.
- 该方法坚持客观性和热力学第二定律.
- 该模型与传统的封闭形状粘性弹性模型相比,显示出更高的性能.
结论:
- 数据驱动的潜力为模拟各种粘弹性材料提供了更大的灵活性.
- 基于NODE的框架为复杂材料行为预测提供了一个强大的方法.
- 这种方法有助于精确模拟诸如脑组织和心肌等材料.
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