Active-learning-based nonintrusive model order reduction

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Qinyu Zhuang, Dirk Hartmann, H. Bungartz, Juan M Lorenzi
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引用次数: 2

Abstract

Abstract Model order reduction (MOR) can provide low-dimensional numerical models for fast simulation. Unlike intrusive methods, nonintrusive methods are attractive because they can be applied even without access to full order models (FOMs). Since nonintrusive MOR methods strongly rely on snapshots of the FOMs, constructing good snapshot sets becomes crucial. In this work, we propose a novel active-learning-based approach for use in conjunction with nonintrusive MOR methods. It is based on two crucial novelties. First, our approach uses joint space sampling to prepare a data pool of the training data. The training data are selected from the data pool using a greedy strategy supported by an error estimator based on Gaussian process regression. Second, we introduce a case-independent validation strategy based on probably approximately correct learning. While the methods proposed here can be applied to different MOR methods, we test them here with artificial neural networks and operator inference.
基于主动学习的非侵入式模型降阶
摘要模型降阶(MOR)可以为快速模拟提供低维数值模型。与侵入式方法不同,非侵入式方法很有吸引力,因为即使不访问全阶模型(FOM),它们也可以应用。由于非侵入性MOR方法强烈依赖于FOM的快照,因此构建良好的快照集变得至关重要。在这项工作中,我们提出了一种新的基于主动学习的方法,与非侵入式MOR方法结合使用。它基于两个关键的新颖性。首先,我们的方法使用联合空间采样来准备训练数据的数据池。使用由基于高斯过程回归的误差估计器支持的贪婪策略从数据池中选择训练数据。其次,我们引入了一种基于近似正确学习的案例独立验证策略。虽然这里提出的方法可以应用于不同的MOR方法,但我们在这里用人工神经网络和算子推理对它们进行了测试。
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
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
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