Accurate Modeling of Antenna Structures by Means of Domain Confinement and Pyramidal Deep Neural Networks

Surrogate‐assisted optimization of 3D printed ceramic nonuniform nonplanar microstrip filter

“…2 One of the viable solution for this challenging problem is the utilization of data-driven surrogate models. [3][4][5][6] Datadriven surrogate models are based on approximating sampled data which allow the model to make a prediction of targeted output characteristics using the given inputs without the need of expert knowledge of the selected problem, high adaptation rate between similar problems, and high computational efficiency. 6,7 Modeling and development of data-driven models is a topic that being studied by many researchers.…”

“…[3][4][5][6] Datadriven surrogate models are based on approximating sampled data which allow the model to make a prediction of targeted output characteristics using the given inputs without the need of expert knowledge of the selected problem, high adaptation rate between similar problems, and high computational efficiency. 6,7 Modeling and development of data-driven models is a topic that being studied by many researchers. Some examples of the commonly used modeling methods for microwave applications from literature can be named as kriging, 8 neural networks, [9][10][11][12] support vector regression, [13][14][15] and deep learning.…”

“…Some examples of the commonly used modeling methods for microwave applications from literature can be named as kriging, 8 neural networks, [9][10][11][12] support vector regression, [13][14][15] and deep learning. 6,7,[16][17][18][19][20][21] Although the utilization of data-driven surrogate models is a viable technique for design and optimization of microwave components, these methods are also has challenging design problems which must be addressed with care. (I) Properly creating data set, either using three-dimensional (3D) EM simulation or experiential data, for model is one of the most crucial step.…”

“…Modeling and development of data‐driven models is a topic that being studied by many researchers. Some examples of the commonly used modeling methods for microwave applications from literature can be named as kriging, 8 neural networks, 9–12 support vector regression, 13–15 and deep learning 6,7,16–21 …”

A Machine Learning Generative Method for Automating Antenna Design and Optimization

“…Meanwhile, a few machine learning algorithms are emerging in antenna area Burrascano et al [1999], jun Zhang et al [2003], Zhu et al [2007], Pastorino and Randazzo [2005], Xiao et al [2018], Cui et al [2020], Xiao et al [2021], Sharma et al [2020], Wu et al [2020], Nan et al [2021], Davoli et al [2021], Zhou et al [2021], Naseri and Hum [2021], Koziel et al [2021], Abdullah and Koziel [2022]. These algorithms can be summarized into two categories: 1) training surrogate models to replace the electromagnetic simulator for acceleration; 2) training inverse design models to propose new candidates.…”