Learning stable reduced-order models for hybrid twins

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2021-06-07 DOI:10.1017/dce.2021.16
Abel Sancarlos, Morgan Cameron, Jean-Marc Le Peuvedic, J. Groulier, J. Duval, E. Cueto, F. Chinesta
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引用次数: 10

Abstract

Abstract The concept of “hybrid twin” (HT) has recently received a growing interest thanks to the availability of powerful machine learning techniques. This twin concept combines physics-based models within a model order reduction framework—to obtain real-time feedback rates—and data science. Thus, the main idea of the HT is to develop on-the-fly data-driven models to correct possible deviations between measurements and physics-based model predictions. This paper is focused on the computation of stable, fast, and accurate corrections in the HT framework. Furthermore, regarding the delicate and important problem of stability, a new approach is proposed, introducing several subvariants and guaranteeing a low computational cost as well as the achievement of a stable time-integration.
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混合双胞胎的学习稳定降阶模型
由于强大的机器学习技术的可用性,“混合双胞胎”(HT)的概念最近受到了越来越多的关注。这个孪生概念结合了模型降阶框架内的基于物理的模型(以获得实时反馈率)和数据科学。因此,高温观测的主要思想是开发实时数据驱动的模型,以纠正测量结果与基于物理的模型预测之间可能存在的偏差。本文的重点是在HT框架下计算稳定、快速和准确的校正。此外,针对复杂而重要的稳定性问题,提出了一种新方法,该方法引入了几个子变量,保证了较低的计算成本和稳定的时间积分。
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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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