统计机器学习工具用于流模型的概率关闭.
J Domingues Lemos1,2, F P Santos3
1ENS de Lyon, CNRS, LPENSL, UMR5672, 69342, Lyon cedex 07, France.
Chaos (Woodbury, N.Y.)
|July 21, 2025
概括
这项研究在牌模型上使用机器学习模拟小规模的流. 这种新的方法改进了缩小模型,为流模拟提供了概率和切线独立的闭合.
科学领域:
- 流体动力学 流体动力学
- 计算物理 计算物理
- 机器学习应用 机器学习应用
背景情况:
- 流模型是复杂的,在分析描述小规模运动方面存在挑战.
- 精确的小尺度建模对于工业应用中高效的数值模拟至关重要.
- 贝模型为模拟流提供了一个计算可处理的方法,保留了关键的纳维埃-斯托克斯属性,如能量级联和间歇性.
研究的目的:
- 为Sabra Shell模型开发封闭,以准确地表示小规模的流运动.
- 利用机器学习技术进行数据驱动的流动力学的建模.
- 创建低调的流模型,这些模型在计算上是高效的,但在统计上是准确的.
主要方法:
- 应用缩放关系到Sabra Shell模型数据以恢复隐藏的对称性和通用统计数据.
- 在重新缩放的数据上使用了变化自动编码器和稀疏的非线性动态识别 (SINDy).
- 生成新的数据实例来关闭缩小模型,只解决更大的运动尺度.
主要成果:
- 为Sabra Shell模型开发了概率和切断独立的闭包.
- 与以前的方法相比,包含机器学习关闭的减少模型显示了更好的统计准确性.
- 减少模型的性能与完全解析的Sabra模拟进行了评估.
结论:
- 机器学习关闭在牌模型框架内有效地模拟小规模的流动力学.
- 拟议的方法为提高流模拟的效率和准确性提供了一个有希望的途径.
- 开发的关闭证明了稳定性,既是概率的,又独立于切断尺度.
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