科学机器学习用于建模和模拟复杂流体
Kyle R Lennon1, Gareth H McKinley2, James W Swan1
1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142.
概括
这项研究引入了一种新的数据驱动框架,用于为复杂流体创建精确的学构成方程. 新的模型灵活,尊重物理定律,在不同的实验条件下工作.
科学领域:
- 风病学和软物质物理学
- 计算流体动力学的流体动力学.
- 材料科学 材料科学 材料科学
背景情况:
- 在工程软材料方面,开发精确的学构成方程至关重要.
- 现有的数据驱动模型与复杂的流体动力学的各种实验数据作斗争.
- 以前的机器学习模型在不同的变形协议之间缺乏可移植性.
研究的目的:
- 提出一个灵活的,数据驱动的框架,用于构建学构成方程.
- 能够创建包含物理约束并独立于实验特点的模型.
- 在复杂的流体动力学中克服经典机器学习的局限性.
主要方法:
- 开发了一个科学机器学习框架,在物质客观的张量构成框架内使用通用近似器.
- 保证模型本质上尊重像框架不变性和张量对称性这样的物理约束.
- 在有限的数据上训练模型并验证它们描述复杂流量的能力.
主要成果:
- 该框架有助于从有限的数据中快速发现准确的组成方程.
- 学习模型可以描述动力学复杂的流动,显示出高度的灵活性.
- 在多维计算流体动力学模拟中成功部署训练模型.
结论:
- 拟议的框架为复杂流体的数据驱动的学建模提供了一个强大的解决方案.
- 这些"数字流体双胞胎"适用于各种材料系统和工程挑战.
- 这种方法促进了机器学习在学和流体动力学中的应用.
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