AIVT:从物理知情的科尔摩戈罗夫-阿诺德网络测量的3D速度数据中推断流热对流
Juan Diego Toscano1, Theo Käufer2,3, Zhibo Wang4
1Division of Applied Mathematics, Brown University, Providence, RI 02912, USA.
Science advances
|May 7, 2025
概括
我们开发了人工智能速度测量-温度测量 (AIVT) 以使用3D速度数据重建流对流中的温度和速度场. 这种人工智能方法从稀疏的实验测量中准确地推断出连续场.
科学领域:
- 流体动力学 流体动力学
- 流研究 流研究
- 科学中的人工智能.
背景情况:
- 流对流在许多自然和工业过程中至关重要.
- 准确,高分辨率测量温度和速度场是具有挑战性的.
- 现有的方法往往提供稀疏或不完整的数据.
研究的目的:
- 开发一种新的AI方法来重建连续的温度和速度场.
- 通过对雷利-贝纳德对流的实验数据验证AI方法.
- 通过人工智能驱动的数据分析来增强对热流的理解.
主要方法:
- 提出了人工智能速度测量-温度测量 (AIVT) 方法.
- 利用了基于物理的科尔摩戈罗夫-阿诺德网络.
- 通过最小化数据,边界条件和控制方程余数来训练AI.
- 应用了AIVT对来自雷利-贝纳德对流的实验3D温度和速度数据.
主要成果:
- AIVT成功地重建了连续和可微分的温度和速度场.
- 该方法从稀疏的数据中推断出瞬间场及其梯度.
- 人工智能结果直接与同时进行的体积实验测量进行了比较.
- 实现了流动态的高分辨率重建.
结论:
- AIVT提供了一种强大的方法来分析流对流.
- 该方法可以从有限的实验数据中进行高分辨率的重建.
- AIVT为推进热流研究提供了一个新的工具.
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