数据驱动的材料建模基于构成关系误差
Pierre Ladevèze1, Ludovic Chamoin1,2
1CentraleSupélec, ENS Paris-Saclay, CNRS, LMPS-Laboratoire de Mécanique Paris-Saclay, Université Paris-Saclay, 4 Avenue des Sciences, 91190 Gif-sur-Yvette, France.
概括
本研究开发了一种一般数据驱动的构成模型,用于elasto-(visco-) 塑料材料,从实验数据中学习. 构成关系错误 (CRE) 框架提供了一个几乎明确的模型配方,即使有测量噪声.
科学领域:
- 固体力学 固体力学是什么
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
背景情况:
- 开发数据驱动的构成模型对于准确模拟材料行为至关重要.
- 现有的模型往往难以将基于物理学的知识与实验数据相结合.
- 在小位移下稳定的材料需要强大的构成模型框架.
研究的目的:
- 确定稳定的elasto-(visco-) 塑料材料的数据驱动构成模型的最一般的数学形式.
- 将物理和材料科学知识纳入数据驱动模型开发中.
- 直接从全场实验测量中学习构成模型.
主要方法:
- 建议采用一般的数据驱动方法,从实验数据中学习构成模型.
- 构成关系错误 (CRE) 被介绍为一个关键工具,数据驱动模型是它的最小化器.
- 开发了一个修改后的CRE,以考虑实验数据中的测量噪声.
主要成果:
- 拟议的数据驱动方法为最佳构成模型提供了准明确的表述.
- 该框架有效地从机械结构的全场测量中学习构成模型.
- 构成关系错误 (CRE) 作为模型学习的有效目标函数.
结论:
- 已经建立了一个基于数据的一般框架,用于从实验数据中学习构成模型.
- 基于CRE的方法为物理知情,数据驱动的材料建模提供了一条道路.
- 该方法强大且适应性强,即使在实验噪声的存在下也是如此.
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