通过规范化流量,为流速度梯度量身定制的模型
M Carbone1,2, V J Peterhans2,3, A S Ecker2,4
1Theoretical Physics I, <a href="https://ror.org/0234wmv40">University of Bayreuth</a>, Bayreuth, Germany.
Physical review letters
|November 15, 2024
概括
本研究引入了一种机器学习方法,使用速度梯度来建模小规模的流. 数据驱动模型准确地捕捉了流统计和动态,与模拟数据密切匹配.
科学领域:
- 流体动力学 流体动力学
- 流建模 流建模
- 计算物理 计算物理
背景情况:
- 小规模的流是复杂的,经常使用随机方程建模.
- 传统模型的准确性在很大程度上依赖于对压力和粘性力的假设.
- 速度梯度为低维流模型提供了一个有希望的基础.
研究的目的:
- 开发一个数据驱动的机器学习模型,用于流中的速度梯度.
- 直接从数据中捕获流的统计性质.
- 创建一个动态系统,复制流统计和时间相关性.
主要方法:
- 利用规范化流来学习直接数值模拟 (DNS) 的速度梯度的概率密度函数 (PDF).
- 在学习PDF的基础上构建了一个确定性动态系统.
- 优化模型时间相关性,使用测量术语匹配DNS数据.
主要成果:
- 机器学习模型成功地学习了速度梯度PDF.
- 由此产生的动态系统表现出所需的稳定状态PDF.
- 模型生成的时间序列在统计属性上与DNS数据非常相似.
结论:
- 使用机器学习的数据驱动方法为流建模提供了强大的替代方案.
- 规范化流是学习流体动力学中复杂概率分布的有效方法.
- 开发的模型在捕捉流统计和动态方面表现出高保真度.
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