A Surrogate Model Based on Artificial Neural Networks for Wave Propagation in Uncertain Media

Abstract

Soil materials can exhibit strongly dispersive properties in the operating frequency range of a physical system, and the uncertain parameters of the dispersive materials introduce uncertainties in the simulation result of propagating waves. It is essential to quantify the uncertainty in the simulation result when the acceptability of these calculation results is considered. To avoid performing thousands of fullwave simulations, an efficient surrogate model based on artificial neural networks (ANNs) is proposed in this paper, to imitate the concerned ground penetrating radar (GPR) calculation. With the autoencoder neural network to reduce the dimensionality of data, the surrogate model successfully predicts the outputs of the GPR calculation using a small number of training samples. The finite-difference time-domain method with the uniaxial perfectly matched layer is used to collect sampling data for the surrogate model. The process of constructing the surrogate model is presented in detail in this paper. The proposed surrogate model is demonstrated to be an attractive alternative to the full-wave GPR calculation due to its considerable advantage in terms of computational expense and speed. INDEX TERMS Artificial neural network (ANN), ground penetrating radar (GPR), surrogate model.

Modeling of the 5G-Band Patch Antennas Using ANNs under the Uncertainty of the Geometrical Design Parameters Associated with the Manufacturing Process

“…The application of ANNs for coding the results of FDTD simulations in the area of electromagnetic (EM) fields was the subject of the literature in the past, e.g., in [17][18][19], where ANN is used to derive surrogate models that conform to the FDTD simulation results. In [17,18], two-stage ANN coding is presented for a case of high dimensionality input data, while in [19], the authors use the ANN to model the scattering parameters of microstrip radio-frequency devices by taking into account deterministic design variables.…”