A General Framework for Building Surrogate Models for Uncertainty Quantification in Computational Electromagnetics

Abstract

In uncertainty analysis, surrogate modeling techniques demonstrate high efficiency and reliable precision in estimating the uncertainty for the finite difference time domain (FDTD) computation. However, building an accurate surrogate model may require a considerable number of system simulations which could be computationally expensive. To reduce such computational cost to build an accurate model, a general framework to build surrogate models for the FDTD computation in the human body based on the least angle regression (LARS) method and the artificial neural network (ANN) is proposed. The LARS method is adapted to dynamically select a number of informative random parameters which are significantly relevant to system outputs. We design a series of convergence criteria for ANN and introduce the adaptive moment estimation (ADAM) optimizer to ANN in order to improve the computational efficiency and accuracy of ANN. This is the first dynamic surrogate modeling technique for the FDTD computation designed by taking both accuracy and computational cost into account.