网格空间映射电磁优化方法的加速策略研究进展

      Progress on acceleration strategies for mesh space mapping-based electromagnetic optimization methods

      • 摘要: 针对三维微波器件优化中粗模型构建复杂、响应不连续以及优化效率低的问题,本文提出了一种多策略协同加速的网格空间映射 (mesh space mapping, MSM) 方法。该方法以粗网格有限元模型为基础,通过三类关键策略协同构建粗模型:首先,利用结构锐化技术对圆角、螺钉等复杂边界进行近似简化,显著降低网格数量与几何建模复杂度;其次,采用径向基函数网格变形方法,使粗模型在几何参数扰动下保持响应连续性与可导性;最后,结合灵敏度分析进行参数筛选,提取粗细模型之间的主要关联变量,从而压缩映射维度并减少训练样本量。基于上述策略,构建了“锐化—变形—筛选”一体化粗模型生成机制,并将其嵌入MSM优化流程。以四阶调谐腔体滤波器为例,验证了所提方法的优化性能:在保持优化精度前提下,整体优化时间由14.4小时降至1.5小时,效率提升近90%。该方法适用于多调谐结构、高维参数空间、响应离散性强的优化任务,为构建自动化、低成本的电磁优化系统提供了可行思路与工程路径。

         

        Abstract: To address the problems of complex coarse model construction, discontinuous responses, and low optimization efficiency in the optimization of three-dimensional microwave devices, this paper proposes a multi-strategy collaboratively accelerated mesh space mapping (MSM) method. This method is based on a coarse-mesh finite element model and constructs the coarse model through the collaboration of three key strategies. First, structure sharpening is employed to approximately simplify complex boundaries such as rounded corners and tuning screws, significantly reducing the number of mesh elements and the complexity of geometric modeling. Second, a radial basis function–based mesh morphing method is adopted to maintain the continuity and differentiability of the coarse model response under geometric parameter perturbations. Third, sensitivity analysis is used for parameter screening to extract the main correlated variables between the coarse and fine models, thereby reducing the mapping dimensionality and the number of required training samples. Based on the above strategies, an integrated “sharpening–morphing–screening” coarse model generation mechanism is constructed and embedded into the MSM optimization process. Using a fourth-order tunable cavity filter as an example, the proposed method is validated: under the premise of maintaining optimization accuracy, the total optimization time is reduced from 14.4 hours to 1.5 hours, achieving nearly 90% improvement in efficiency. This method is applicable to optimization tasks with multiple tuning structures, high-dimensional parameter spaces, and strongly discontinuous responses, providing a feasible approach and engineering path for building automated and low-cost electromagnetic optimization systems.

         

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