基于POD、DMD和ANN的开关控制加热系统非侵入式数据学习

IF 1 4区工程技术 Q4 MECHANICS

Comptes Rendus Mecanique Pub Date : 2019-11-01 DOI:10.1016/j.crme.2019.11.005

Tarik Fahlaoui, Florian De Vuyst

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引用次数: 1

摘要

本工作的目的是从有限元求解器生成的数据中推导出二维开关控制加热系统的精确模型。非侵入式方法应该能够捕获温度场、动力学和潜在的开关控制规则。为了实现这一目标，本文提出的算法将利用三种主要成分:固有正交分解(POD)、动态模态分解(DMD)和人工神经网络(ANN)。将给出一些数值结果，并与高保真数值解进行比较，以证明该方法能够再现动力学。

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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Nonintrusive data-based learning of a switched control heating system using POD, DMD and ANN

The aim of this work is to derive an accurate model of two-dimensional switched control heating system from data generated by a Finite Element solver. The nonintrusive approach should be able to capture both temperature fields, dynamics and the underlying switching control rule. To achieve this goal, the algorithm proposed in this paper will make use of three main ingredients: proper orthogonal decomposition (POD), dynamic mode decomposition (DMD) and artificial neural networks (ANN). Some numerical results will be presented and compared to the high-fidelity numerical solutions to demonstrate the capability of the method to reproduce the dynamics.

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来源期刊

Comptes Rendus Mecanique 物理-力学

CiteScore

1.40

自引率

0.00%

发文量

审稿时长

12 months

期刊介绍： The Comptes rendus - Mécanique cover all fields of the discipline: Logic, Combinatorics, Number Theory, Group Theory, Mathematical Analysis, (Partial) Differential Equations, Geometry, Topology, Dynamical systems, Mathematical Physics, Mathematical Problems in Mechanics, Signal Theory, Mathematical Economics, … The journal publishes original and high-quality research articles. These can be in either in English or in French, with an abstract in both languages. An abridged version of the main text in the second language may also be included.