Data repairing and resolution enhancement using data-driven modal decomposition and deep learning

IF 2.8 2区 工程技术 Q2 ENGINEERING, MECHANICAL
Ashton Hetherington , Daniel Serfaty , Adrián Corrochano , Julio Soria , Soledad Le Clainche
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引用次数: 0

Abstract

This paper introduces a new series of methods which combine modal decomposition algorithms, such as singular value decomposition and high-order singular value decomposition, and deep learning architectures to repair, enhance, and increase the quality and precision of numerical and experimental data. A combination of two- and three-dimensional, numerical and experimental datasets are used to demonstrate the reconstruction capacity of the presented methods, showing that these methods can be used to reconstruct any type of dataset, showing outstanding results when applied to highly complex data, which is noisy. The combination of benefits of these techniques results in a series of data-driven methods which are capable of repairing and/or enhancing the resolution of a dataset by identifying the underlying physics that define the data, which is incomplete or under-resolved, filtering any existing noise. These methods and the Python codes are included in the first release of ModelFLOWs-app.1

利用数据驱动的模态分解和深度学习进行数据修复和分辨率提升
本文介绍了一系列结合模态分解算法(如奇异值分解和高阶奇异值分解)和深度学习架构的新方法,用于修复、增强和提高数值与实验数据的质量和精度。本文结合二维和三维、数值和实验数据集,展示了所介绍方法的重构能力,表明这些方法可用于重构任何类型的数据集,在应用于高复杂度、高噪声数据时效果显著。这些技术的优点结合在一起,产生了一系列数据驱动的方法,它们能够通过识别定义数据的基本物理特性,过滤任何现有噪声,修复和/或提高数据集的分辨率。这些方法和 Python 代码包含在第一版 ModelFLOWs-app.1 中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Experimental Thermal and Fluid Science
Experimental Thermal and Fluid Science 工程技术-工程:机械
CiteScore
6.70
自引率
3.10%
发文量
159
审稿时长
34 days
期刊介绍: Experimental Thermal and Fluid Science provides a forum for research emphasizing experimental work that enhances fundamental understanding of heat transfer, thermodynamics, and fluid mechanics. In addition to the principal areas of research, the journal covers research results in related fields, including combined heat and mass transfer, flows with phase transition, micro- and nano-scale systems, multiphase flow, combustion, radiative transfer, porous media, cryogenics, turbulence, and novel experimental techniques.
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