Knowledge-based coarse and fine mesh space mapping approach to EM optimization

F. Feng, Chao Zhang, Venu-Madhav-Reddy Gongal-Reddy, Qi-jun Zhang
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引用次数: 12

Abstract

Space mapping is an effective method for speeding up EM optimization. The method normally requires an equivalent circuit as the coarse model. This paper addresses the situation when an equivalent circuit coarse model is not available. We establish our coarse model using a lookup table to store the data of coarse mesh EM simulations and its derivatives, avoiding the EM re-simulations w.r.t. the same values of design variables. In the proposed method, the surrogate model is developed using knowledge-based neural network (KBNN) combining the coarse model with a neural network. Our technique uses mostly coarse mesh EM evaluation and occasionally fine mesh EM evaluation to achieve optimal EM solutions with fine mesh accuracy. This technique is illustrated by two microwave filter examples.
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基于知识的粗、细网格空间映射方法的电磁优化
空间映射是加速电磁优化的有效方法。该方法通常需要一个等效电路作为粗模型。本文解决了没有等效电路粗模型的情况。我们使用查找表来建立粗网格模型,以存储粗网格电磁仿真及其导数的数据,避免在相同的设计变量值下进行电磁重新模拟。在该方法中,采用基于知识的神经网络(KBNN)将粗模型与神经网络相结合来建立代理模型。我们的技术主要使用粗网格EM评估,偶尔使用细网格EM评估,以获得具有细网格精度的最佳EM解决方案。通过两个微波滤波器实例说明了该技术。
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