Fast and Intelligent Antenna Design Optimization using Machine Learning

Gopinath Gampala; C. J. Reddy

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Fast and Intelligent Antenna Design Optimization using Machine Learning

机译：使用机器学习的快速和智能天线设计优化

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Traditional antenna optimization solves themodified version of the original antenna design for each iteration.Thus, the total time required to optimize a given antenna design ishighly dependent on the convergence criteria of the selectedalgorithm and the time taken for each iteration. The use ofmachine learning enables the antenna designer to generate trainedmathematical model that replicates the original antenna designand then apply optimization on the trained model. Use of trainedmodel allows to run thousands of optimization iterations in a spanof few seconds.

机译：传统的天线优化解决了每个迭代的原始天线设计的修改版本。因此，优化给定天线设计所需的总时间是高度依赖所选的收敛标准算法和每次迭代所花费的时间。指某东西的用途机器学习使天线设计师能够生成培训复制原始天线设计的数学模型然后在训练模型上应用优化。使用训练有素模型允许在跨度中运行数千个优化迭代几秒钟。

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来源
《Applied Computational Electromagnetics Society journal》 |2020年第11期|1350-1351|共2页
作者
Gopinath Gampala; C. J. Reddy;
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作者单位

Altair Engineering Inc.Phoenix AZ USA;

Altair Engineering Inc.Phoenix AZ USA;

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原文格式 PDF
正文语种 eng
中图分类
关键词
antenna optimization; design of experiments; machine learning; regression;

机译：天线优化;实验设计;机器学习;回归;

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Fast and Intelligent Antenna Design Optimization using Machine Learning

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