基于机器学习的微波被动器的全球优化,具有可变可信度EM模型和响应特征
Slawomir Koziel1,2, Anna Pietrenko-Dabrowska3
1Engineering Optimization & Modeling Center, Reykjavik University, 102, Reykjavík, Iceland. koziel@ru.is.
Scientific reports
|March 16, 2024
概括
本研究提出了一个具有成本效益的机器学习框架,用于优化微波被动元件. 它使用可变真实模拟和响应功能来降低计算成本,实现竞争力的设计质量.
科学领域:
- 电气工程 电气工程
- 计算电磁学 计算机电磁学
- 机器学习应用 机器学习应用
背景情况:
- 优化微波被动元件对于复杂的现代电路至关重要.
- 全波电磁 (EM) 模拟对于全球优化而言是计算上昂贵的.
- 现有的方法与非线性特征和高计算需求作斗争.
研究的目的:
- 为微波被动元件开发一个具有成本效益的全球参数调技术.
- 为了解决传统的EM驱动优化的计算负担.
- 提高基于机器学习的设计优化效率.
主要方法:
- 利用可变真实性的电磁 (EM) 模拟.
- 在基于战争的机器学习框架中使用响应功能技术.
- 使用一个协同战斗的代孕模型和一个粒子群集优化器.
主要成果:
- 拟议的框架实现了具有竞争力的设计质量和计算成本.
- 与基准方法 (通常是60个) 相比,它需要更少的高可靠性EM分析.
- 证明了对自然启发的算法,梯度搜索和直接ML技术的有效性.
结论:
- 这种创新技术为微波被动元件参数调节提供了高效的解决方案.
- 可变真实模拟和响应特征可以降低计算成本.
- 该框架为复杂电路设计优化提供了一个实用的方法.
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