An Uncertainty Analysis on Finite Difference Time-Domain Computations With Artificial Neural Networks: Improving accuracy while maintaining low computational costs-Reference-Cited by-同舟云学术

An Uncertainty Analysis on Finite Difference Time-Domain Computations With Artificial Neural Networks: Improving accuracy while maintaining low computational costs

Published:2023-02 Issue:1 Volume:65 Page:60-70
ISSN:1045-9243
Container-title:IEEE Antennas and Propagation Magazine
language:
Short-container-title:IEEE Antennas Propag. Mag.

Author:

Hu Runze¹^ORCID,Monebhurrun Vikass²^ORCID,Himeno Ryutaro³,Yokota Hideo⁴,Costen Fumie⁵^ORCID

Affiliation:

1. Postdoctoral Fellow, Tsinghua University, Beijing, China

2. CentraleSupélec, Gif-sur-Yvette, France

3. Specially Appointed Professor, Juntendo University, Tokyo, Japan

4. Image Processing Research Team Leader, Center for Advanced Photonics RIKEN, Wako¯, Saitama, Japan

5. University of Manchester, Manchester, U.K.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Condensed Matter Physics

Link

http://xplorestaging.ieee.org/ielx7/74/10035820/09714142.pdf?arnumber=9714142

Reference45 articles.

1. Minkowski Sum Method for Planar Arrays Sensitivity Analysis With Uncertain-But-Bounded Excitation Tolerances

2. Probabilistic Interval Analysis for the Analytic Prediction of the Pattern Tolerance Distribution in Linear Phased Arrays With Random Excitation Errors

3. Perfectly matched layer media with CFS for an unconditionally stable ADI-FDTD method

4. Sparse Linear Regression (SPLINER) Approach for Efficient Multidimensional Uncertainty Quantification of High-Speed Circuits

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