高斯过程仿真下非侵入降阶模型的多相流应用

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2021-11-15 DOI:10.1017/dce.2022.19
T. Botsas, Indranil Pan, L. Mason, O. Matar
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引用次数: 2

摘要

摘要降阶模型(ROM)是高保真度复杂模型的计算廉价的简化。这种模型可以在计算流体动力学中找到,在计算流体力学中,它们可以用于预测多相流的特性。在之前的工作中,我们提出了一个ROM分析框架,该框架将压缩技术(如自动编码器)与潜在空间中的高斯过程回归相结合。与标准编码-解码例程相比,这种配对具有显著优势,例如能够在初始条件空间中进行插值或外推,即使在模拟数据不可用的情况下,也可以提供预测。在这项工作中,我们专注于这一主要优势,并通过在三个多相流应用中执行管道来展示其有效性。我们还通过使用深度高斯过程作为插值算法来扩展该方法,并比较我们的两种变体以及文献中使用长短期记忆网络的另一种变体的插值性能。
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Multiphase flow applications of nonintrusive reduced-order models with Gaussian process emulation
Abstract Reduced-order models (ROMs) are computationally inexpensive simplifications of high-fidelity complex ones. Such models can be found in computational fluid dynamics where they can be used to predict the characteristics of multiphase flows. In previous work, we presented a ROM analysis framework that coupled compression techniques, such as autoencoders, with Gaussian process regression in the latent space. This pairing has significant advantages over the standard encoding–decoding routine, such as the ability to interpolate or extrapolate in the initial conditions’ space, which can provide predictions even when simulation data are not available. In this work, we focus on this major advantage and show its effectiveness by performing the pipeline on three multiphase flow applications. We also extend the methodology by using deep Gaussian processes as the interpolation algorithm and compare the performance of our two variations, as well as another variation from the literature that uses long short-term memory networks, for the interpolation.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
期刊最新文献
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