基于物理感知神经网络的带涂层部件电磁分析参数模型阶次缩减

IF 8.7 2区 工程技术 Q1 Mathematics
SiHun Lee, Seung-Hoon Kang, Sangmin Lee, SangJoon Shin
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引用次数: 0

摘要

有限元(FE)分析是预测电磁场散射最精确的方法之一,但其计算开销很大。在本研究中,我们提出了一种数据驱动的参数模型阶次缩减(pMOR)框架,用于预测有限元分析的散射电磁场。我们选择涂层部件的表面阻抗作为分析参数。在数据驱动的 pMOR 方法中,选择了将物理感知(PA)神经网络纳入最小二乘分层变异自动编码器(LSH-VAE)。所提出的 PA-LSH-VAE 框架可直接访问由大量自由度 (DOF) 表示的散射电磁场。此外,它还能捕捉复值多参数变化的行为。采用并行计算方法可高效生成训练数据。PA-LSH-VAE 框架可处理超过 200 万个 DOF,提供令人满意的精度,并表现出二阶加速因子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Physics-aware neural network-based parametric model-order reduction of the electromagnetic analysis for a coated component

Physics-aware neural network-based parametric model-order reduction of the electromagnetic analysis for a coated component

Finite element (FE) analysis is one of the most accurate methods for predicting electromagnetic field scatter; however, it presents a significant computational overhead. In this study, we propose a data-driven parametric model-order reduction (pMOR) framework to predict the scattered electromagnetic field of FE analysis. The surface impedance of a coated component is selected as parameter of analysis. A physics-aware (PA) neural network incorporated within a least-squares hierarchical-variational autoencoder (LSH-VAE) is selected for the data-driven pMOR method. The proposed PA-LSH-VAE framework directly accesses the scattered electromagnetic field represented by a large number of degrees of freedom (DOFs). Furthermore, it captures the behavior along with the variation of the complex-valued multi-parameters. A parallel computing approach is used to generate the training data efficiently. The PA-LSH-VAE framework is designed to handle over 2 million DOFs, providing satisfactory accuracy and exhibiting a second-order speed-up factor.

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来源期刊
Engineering with Computers
Engineering with Computers 工程技术-工程:机械
CiteScore
16.50
自引率
2.30%
发文量
203
审稿时长
9 months
期刊介绍: Engineering with Computers is an international journal dedicated to simulation-based engineering. It features original papers and comprehensive reviews on technologies supporting simulation-based engineering, along with demonstrations of operational simulation-based engineering systems. The journal covers various technical areas such as adaptive simulation techniques, engineering databases, CAD geometry integration, mesh generation, parallel simulation methods, simulation frameworks, user interface technologies, and visualization techniques. It also encompasses a wide range of application areas where engineering technologies are applied, spanning from automotive industry applications to medical device design.
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