Efficient Antenna Modeling and Optimization Using Multifidelity Stacked Neural Network
Ju Tan, Yu Shao, Jiliang Zhang, Jie Zhang
Abstract
In this work, a multi-fidelity stacked neural network (MFSNN) is proposed to construct surrogate model for antenna modelling and optimisation. The stacked neural network consists of a low-fidelity (LF) network, a linear high-fidelity (HF) network and a non-linear HF network. By learning the prior from sufficient computationally cheap LF data, the MFSNN has significantly reduced the requirement of computationally expansive HF data. The correlation between LF and HF models can be learned adaptively and accurately by decomposing the correlation into linear component and non-linear component. The feasibility of the approach is validated by two antenna structures which shows that the MFSNN based surrogation model can make predictions for broad ranges of input parameters with satisfactory accuracy. Then the surrogate model is directly applied in the particle swarm optimisation (PSO) framework to replace the full-wave simulation and accelerate antenna optimisation procedure.