Artificial Neural Networks for Parametric Electromagnetic Modeling and Optimization

This chapter introduces both the fundamentals and advanced formulations of artificial neural network (ANN) techniques for parametric electromagnetic (EM) modeling and optimization. ANN is an information handling system whose design was enlightened by the investigation into the human brain's capacity to learn from observations and to summarize through abstraction. ANN is an acknowledged tool for parametric EM modeling and optimization, that is, using geometric parameters as variables to represent the EM behavior. Direct methods for EM design optimization are generally computationally expensive and require repeated EM evaluations due to constantly changing geometry. ANN has become an effective method for EM parametric modeling by learning the relationship between EM conducts and geometric parameters. The ANN after training can quickly solve the EM behavior of microwave devices when the geometric parameters change repeatedly. If a neural network has multiple hidden layers, it is defined as a deep neural network. In modeling highly complicated sophisticated relationships, such as modeling with high-dimensional filters with numerous input variables, deep neural networks can do better than shallow neural networks (neural networks with only a few hidden layers). Exploiting the availability of prior knowledge for parametric EM modeling, knowledge-based neural networks (KBNNs) have been exploited. Compared with traditional ANN, KBNN can achieve identical modeling precision with fewer training data and offer preferable extrapolation, thus accelerating model development and improving the generalization ability of parametric EM modeling and optimization. A progressive knowledge-based modeling method, combining neural networks and transfer functions (neuro-transfer functions or neuro- transfer function [ TF s]), has been exploited for parametric modeling of EM responses. Since an appropriate equivalent circuit model/experience model may not be exploitable in certain cases, the neuro-TF approach is capable of using the transfer function as the prior knowledge. The ANN-based parametric EM models can be further utilized as surrogate models for EM optimizations. The exploration of ANN techniques for parametric EM modeling and optimization is a hot topic and continues to be an open and strategic direction.

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