Space mapping is a recognized surrogate-based optimization method to accelerate the electromagnetic (EM) design. In this article, for the first time, space mapping is elevated from solving the problem of EM optimization to solving the problem of multiphysics optimization for high power microwave filters. Multiphysics analysis, which involves the EM domain with other physics domains, is increasingly important for high-performance microwave components to obtain an accurate system design. To speed up the multiphysics design, a space-mapping-based surrogate model including a coarse model and two mapping functions is proposed in this article. We propose to use EM single physics responses as the coarse model to provide good approximations to fine model multiphysics responses. To avoid repetitive EM simulations during the surrogate model training and optimization process, the coarse model is developed using an artificial neural network (ANN). Frequency mapping and explicit input mapping are further performed to develop the proposed surrogate model. Multiple EM and multiphysics training samples are evaluated in parallel to develop the surrogate model. A trust-region algorithm, tailored to the space-mapping-based multiphysics optimization technique, is proposed to improve the convergence. By exploiting the knowledge of the coarse model established by relatively inexpensive EM data, the proposed technique can provide a larger and more efficient optimization update in each optimization iteration, consequently obtaining optimal solutions faster than the existing multiphysics optimization without space mapping. Two examples of multiphysics optimization of high-power microwave filters are used to validate the proposed technique.

中文翻译：

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高功率微波滤波器基于并行空间映射的高级多物理场优化

空间映射是公认的基于代理的优化方法，可以加快电磁（EM）设计。本文首次将空间映射从解决EM优化问题提升为解决大功率微波滤波器的多物理场优化问题。涉及到EM域和其他物理域的多物理场分析对于高性能微波组件获得准确的系统设计越来越重要。为了加快多物理场设计的速度，本文提出了一种基于空间映射的代理模型，该模型包括一个粗模型和两个映射函数。我们建议使用EM单物理场响应作为粗略模型，以便为精细模型的多物理场响应提供良好的近似值。为了避免在替代模型训练和优化过程中进行重复的EM仿真，使用人工神经网络（ANN）开发了粗略模型。进一步执行频率映射和显式输入映射以开发建议的代理模型。并行评估多个EM和多物理场训练样本以开发代理模型。提出了一种基于空间映射的多物理场优化技术的信任区域算法，以提高收敛性。通过利用相对便宜的EM数据建立的粗略模型的知识，所提出的技术可以在每次优化迭代中提供更大，更有效的优化更新，因此比没有空间映射的现有多物理场优化方法更快地获得了最佳解决方案。