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

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 whic...

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Bibliographic Details
Published in:IEEE transactions on antennas and propagation 2022-02, Vol.70 (2), p.1402-1414
Main Authors: Hu, Runze, Monebhurrun, Vikass, Himeno, Ryutaro, Yokota, Hideo, Costen, Fumie
Format: Article
Language:English
Subjects:
Accuracy
Adaptation models
Artificial neural network (ANN)
Artificial neural networks
bioelectromagnetics
biological tissues
Computational efficiency
Computational electromagnetics
Computational modeling
Computing costs
cross-validation
Data models
debye media
Electromagnetism
Engineering Sciences
Estimation
Finite difference methods
finite difference time domain (FDTD)
Finite difference time domain method
least angle regression (LARS)
Mathematical model
Time-domain analysis
Uncertainty
Uncertainty analysis
uncertainty quantification (UQ)
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Summary: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.
ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2021.3111333