基于数据的方法用于与时间相关的流模型的关闭
J Domingues Lemos1, A A Mailybaev2
1Universidade Federal do Rio de Janeiro, Rua Antônio Barros de Castro, 119 Cidade Universitária, Rio de Janeiro, RJ 21941-853, Brazil.
Physical review. E
|March 16, 2024
概括
这项研究使用外模型开发了新的流关闭,提供了一个理论上有基础的方法来捕捉概率流体动力学特征. 新方法通过近似概率密度函数来增强模拟,以提高准确性.
科学领域:
- 流体动力学 流体动力学
- 计算物理 计算物理
- 统计力学 统计力学
背景情况:
- 流体运动缺乏完整的分析描述,需要现象学模型用于实际应用,如数值模拟.
- 当前的方法经常使用小规模的闭包,这可能无法完全捕捉流的固有概率性质.
研究的目的:
- 使用外模型,构建一个具有强大的理论基础的流封闭.
- 纳入并捕捉流的内在概率特征.
- 根据概率密度函数开发与时间相关的关闭.
主要方法:
- 使用了Sabra外风暴模型.
- 重缩模型变量以揭示隐藏的对称性和通用分布.
- 采用高斯混合模型来近似关闭结构的概率密度函数.
- 开发了一个整合机器学习工具的框架.
主要成果:
- 成功构建了流的外模型的概率闭包.
- 通过近似概率密度函数来实现与时间相关的关闭.
- 展示了一种提高流建模理论基础的方法.
结论:
- 开发的闭包提供了一个理论上强大的和概率的方法来建模流.
- 该方法提供了一个框架,用于将先进的机器学习技术集成到流模拟中.
- 这项工作促进了复杂流体动力学的理解和模拟.
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