Electromagnetic interaction complexity reduction using deep learnin

Download

index.pdf

Date

2019

Author

Karaosmanoğlu, Barışcan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

250
views

315
downloads

In this thesis, we present a novel approach to accelerate electromagnetic simulations by the multilevel fast multipole algorithm (MLFMA). The strategy is based on a progressive elimination of electromagnetic interactions, resulting in trimmed tree structures, during iterative solutions. To systematically perform such eliminations, artificial neural network (ANN) models are constructed and trained to estimate errors in updated surface current coefficients. These column eliminations are supported by straightforward row eliminations, leading to increasingly sparse tree structures and matrix equations as iterations continue. We show that the proposed implementation, namely trimmed MLFMA (T-MLFMA), leads to significantly accelerated electromagnetic simulations of large-scale objects, while the accuracy is still much better than the high-frequency techniques. T-MLFMA can be seen as an exemplar of implementations, where machine learning is successfully integrated into an electromagnetic solver for enhanced simulations.

Subject Keywords

Machine learning, Machine learning Industrial applications., Keywords: Integral equations, machine learning, multilevel fast multipole algorithm (MLFMA)

URI

http://etd.lib.metu.edu.tr/upload/12625077/index.pdf
https://hdl.handle.net/11511/45169

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Rigorous Analysis of Double-Negative Materials with the Multilevel Fast Multipole Algorithm Ergül, Özgür Salih (2012-02-01) We present rigorous analysis of double-negative materials (DNMs) with surface integral equations and the multilevel fast multipole algorithm (MLFMA). Accuracy and efficiency of numerical solutions are investigated when DNMs are formulated with two recently developed formulations, i.e., the combined tangential formulation (CTF) and the electric and magnetic current combined-field integral equation (JMCHE). Simulation results on canonical objects are consistent with previous results in the literature on ordin...
Rigorous Analysis of Deformed Nanowires Using the Multilevel Fast Multipole Algorithm Karaosmanoglu, Bariscan; Yilmaz, Akif; Ergül, Özgür Salih (2015-05-17) We present accurate full-wave analysis of deformed nanowires using a rigorous simulation environment based on the multilevel fast multipole algorithm. Single nanowires as well as their arrays are deformed randomly in order to understand the effects of deformations to scattering characteristics of these structures. Results of hundreds of simulations are considered for statistically meaningful analysis of deformation effects. We show that deformations significantly enhance the forward-scattering abilities of ...
Electromagnetic modeling of split-ring resonators Gurel, Levent; Unal, Alper; Ergül, Özgür Salih (2006-09-15) In this paper, we report our efforts to model split-ring resonators (SRRs) and their large arrays accurately and efficiently in a sophisticated simulation environment based on recent advances in the computational electromagnetics. The resulting linear system obtained from the simultaneous discretization of the geometry and Maxwell's equations is solved iteratively with the multilevel fast multipole algorithm. As an example, we present an array of 125 SRRs showing a negative effective permeability about 92 GHz.
Error prediction in electromagnetic simulations using machine learning KARAOSMANOGLU, BARISCAN; Ergül, Özgür Salih (2019-07-01) © 2019 IEEE.We present a novel approach of using deep convolutional neural networks (CNN) to predict electromagnetic scattering errors in iterative solutions of electrically large three-dimensional objects. Deep CNN models are constructed and trained by using surface current images to predict far-zone scattering errors. Numerical experiments demonstrate successful predictions with more than 95% accuracy. The constructed models can be useful to quickly assess the accuracy of candidate solutions of current di...
Efficient and Accurate Electromagnetic Optimizations Based on Approximate Forms of the Multilevel Fast Multipole Algorithm Onol, Can; Karaosmanoglu, Bariscan; Ergül, Özgür Salih (2016-01-01) We present electromagnetic optimizations by heuristic algorithms supported by approximate forms of the multilevel fast multipole algorithm (MLFMA). Optimizations of complex structures, such as antennas, are performed by considering each trial as an electromagnetic problem that can be analyzed via MLFMA and its approximate forms. A dynamic accuracy control is utilized in order to increase the efficiency of optimizations. Specifically, in the proposed scheme, the accuracy is used as a parameter of the optimiz...

Citation Formats

B. Karaosmanoğlu, “Electromagnetic interaction complexity reduction using deep learnin,” Thesis (Ph.D.) -- Graduate School of Natural and Applied Sciences. Electrical and Electronics Engineering., Middle East Technical University, 2019.