English(EN) Machine Learning Assisted Inverse Design of Pixelated mmWave Patch Antennas

机器学习辅助毫米波贴片天线的逆向设计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-25 04:00

研究人员开发了一个用于设计像素化毫米波贴片天线的机器学习框架。该系统使用XGBoost分类器在模拟前过滤掉非谐振天线模式，提高了效率。然后，一个混合CNN-BiLSTM模型预测天线的响应，并且一个逆向设计模型生成像素模式以满足特定的S11规范，展示了自动天线设计能力。 AI

影响这项研究展示了机器学习如何加速天线等专用硬件组件的设计和优化。

排序理由这是一篇研究论文，详细介绍了用于天线设计的新型机器学习方法。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 1 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 这是一篇研究论文，详细介绍了用于天线设计的新型机器学习方法。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.AI TIER_1 English(EN) · Nadeem Rather, Holger Claussen, Lester Ho · 2026-08-25 04:00

机器学习辅助像素化毫米波贴片天线的逆向设计

arXiv:2608.23469v1 Announce Type: cross Abstract: A machine learning-assisted framework for the inverse design of pixelated millimetre-wave patch antennas targeting the 22--30 GHz band is presented. The antenna surface is represented as a 19x23 binary pixel grid on a Rogers RT/du…