用算法微分法计算电磁整形中的形状导数

IF 0.8 4区 数学
Karsten Eppler sci
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引用次数: 1

摘要

同时,基于分析形状导数的形状优化在工程应用中是一种成熟的工具。对于潜在问题的适当离散化,算法微分技术也可以用于提供分析形状导数的离散模拟。本文关注两种类型的梯度计算的比较,以及它们对基于梯度的优化方法在精度和性能方面的影响,因为到目前为止,只有少数尝试对这些方法进行比较。为此,本文讨论了这两种技术,并分析了电磁成形通用测试用例的数值结果。由于发现两种方法都很好地一致,算法微分似乎也值得用于要求更高的形状优化问题。AMS受试者分类:49M25、49Q10、78M15
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computation of Shape Derivatives in Electromagnetic Shaping by Algorithmic Differentiation
Shape optimization based on analytical shape derivatives is meanwhile a well-established tool in engineering applications. For an appropriate discretization of the underlying problem, the technique of algorithmic differentiation can also be used to provide a discrete analogue of the analytic shape derivative. The present article is concerned with the comparison of both types of gradient calculation and their effects on a gradient-based optimization method with respect to accuracy and performance, since so far only a few attempts have been made to compare these approaches. For this purpose, the article discusses both techniques and analyses the obtained numerical results for a generic test case from electromagnetic shaping. Since good agreement of both methods is found, algorithmic differentiation seems to be worthwhile to be used also for more demanding shape optimization problems. AMS subject classifications: 49M25, 49Q10, 78M15
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来源期刊
数学研究
数学研究 MATHEMATICS-
自引率
0.00%
发文量
1109
期刊介绍: Journal of Mathematical Study (JMS) is a comprehensive mathematical journal published jointly by Global Science Press and Xiamen University. It publishes original research and survey papers, in English, of high scientific value in all major fields of mathematics, including pure mathematics, applied mathematics, operational research, and computational mathematics.
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