2026-10-14T12:59:43Z https://www.scidb.cn/oai

10.57760/sciencedb.space.00546 2023-07-27T09:42:51Z

2023-07-27 State-of-the-Art: AI-Assisted Surrogate Modeling and Optimization for Microwave Filters doi:10.57760/sciencedb.space.00546 en Microwave filters are indispensable passive devices for modern wireless communication systems. Nowadays, electromagnetic (EM) simulation-based design process is a normfor filter designs. Many EM-based design methodologies for microwave filter design have emerged in recent years to achieve efficiency, automation, and customizability. The majority ofEM-based design methods exploit low-cost models (i.e., surrogates) in various forms, and artificial intelligence techniques assist the surrogate modeling and optimization processes. Focusingon surrogate-assisted microwave filter designs, this article first analyzes the characteristic of filter design based on different design objective functions. Then, the state-of-the-art filter design methodologies are reviewed, including surrogate modeling (machine learning) methods and advanced optimization algorithms. Three essential techniques in filter designs are included:1) smart data sampling techniques; 2) advanced surrogate modeling techniques; and 3) advanced optimization methods and frameworks. To achieve success and stability, they have to be tailored or combined together to achieve the specific characteristics of the microwave filters. Finally, new emerging design applications and future trends in the filter design are discussed. Artificial intelligent (AI); computer-aided design (CAD); coupling matrix; design knowledge; electromagnetic (EM)-simulation based design; machine learning; microwave filters; optimization; surrogate modeling Yang Yu Zhen Zhang Qingsha S. Cheng Bo Liu Yi Wang Cheng Guo Terry Tao Ye RESTRICTED dataset Science Data Bank