分析和人工智能发现了稳定,准确和可泛化地质物理流的子电网规模关闭
Karan Jakhar1,2, Yifei Guan1,3, Pedram Hassanzadeh4
1University of Chicago, Department of Geophysical Sciences, Chicago, Illinois 60637, USA.
Physical review letters
|March 1, 2026
概括
我们使用流体物理学开发了一种新的人工智能驱动的流关闭. 这种基于人工智能的方法改进了大模拟,为2D流模型提供了准确和稳定的结果.
科学领域:
- 流体动力学 流体动力学
- 计算物理 计算物理
- 人工智能的人工智能
背景情况:
- 流模型对于流体动力学模拟至关重要.
- 现有的大型模拟模型经常面临稳定性问题.
- 人工智能为发现流关闭提供了新的途径.
研究的目的:
- 为了发现一种新的2D流的封闭式封闭.
- 为了提高大模拟的准确性和稳定性.
- 探索AI在流体物理学中的应用.
主要方法:
- 将人工智能与流体物理原理结合起来.
- 使用小型直接数值模拟数据集.
- 采用稀疏方程发现与第四阶泰勒扩展.
主要成果:
- 发现了一种新的,准确的,稳定的2D流的闭式闭合.
- 使用新封闭的大端模拟重现了直接的数值模拟统计数据,包括极端事件.
- 关闭来自于第四阶段的泰勒扩张,与之前的第二阶段方法不同.
结论:
- 发现的关闭增强了大模拟的预测能力.
- 考虑到跨尺度的能量传输是开发改进的流模型的关键.
- 这项工作表明了人工智能在推进流体动力学研究方面的潜力.
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