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Article: Machine-Learning-Based PML for the FDTD Method

TitleMachine-Learning-Based PML for the FDTD Method
Authors
KeywordsFinite difference methods
Time-domain analysis
Computational modeling
Neurons
Data models
Issue Date2019
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7727
Citation
IEEE Antennas and Wireless Propagation Letters, 2019, v. 18 n. 1, p. 192-196 How to Cite?
AbstractIn this letter, a novel absorbing boundary condition (ABC) computation method for finite-difference time-domain (FDTD) is proposed based on the machine learning approach. The hyperbolic tangent basis function (HTBF) neural network is introduced to replace traditional perfectly matched layer (PML) ABC during the FDTD solving process. The field data on the interface of conventional PML are employed to train HTBF-based PML model. Compared to the conventional approach, the novel method greatly decreases the size of a computation domain and the computation complexity of FDTD because the new model only involves the one-cell boundary layer. Numerical examples are provided to benchmark the performance of the proposed method. The results demonstrate that the newly proposed method could replace conventional PML and could be integrated into FDTD solving process with satisfactory accuracy and compatibility to FDTD. According to our knowledge, this proposed model combined artificial neural network (ANN) model is an unreported new approach based on a machine learning based for FDTD.
Persistent Identifierhttp://hdl.handle.net/10722/278141
ISSN
2019 Impact Factor: 3.726
2015 SCImago Journal Rankings: 1.901

 

DC FieldValueLanguage
dc.contributor.authorYAO, H-
dc.contributor.authorJiang, L-
dc.date.accessioned2019-10-04T08:08:17Z-
dc.date.available2019-10-04T08:08:17Z-
dc.date.issued2019-
dc.identifier.citationIEEE Antennas and Wireless Propagation Letters, 2019, v. 18 n. 1, p. 192-196-
dc.identifier.issn1536-1225-
dc.identifier.urihttp://hdl.handle.net/10722/278141-
dc.description.abstractIn this letter, a novel absorbing boundary condition (ABC) computation method for finite-difference time-domain (FDTD) is proposed based on the machine learning approach. The hyperbolic tangent basis function (HTBF) neural network is introduced to replace traditional perfectly matched layer (PML) ABC during the FDTD solving process. The field data on the interface of conventional PML are employed to train HTBF-based PML model. Compared to the conventional approach, the novel method greatly decreases the size of a computation domain and the computation complexity of FDTD because the new model only involves the one-cell boundary layer. Numerical examples are provided to benchmark the performance of the proposed method. The results demonstrate that the newly proposed method could replace conventional PML and could be integrated into FDTD solving process with satisfactory accuracy and compatibility to FDTD. According to our knowledge, this proposed model combined artificial neural network (ANN) model is an unreported new approach based on a machine learning based for FDTD.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7727-
dc.relation.ispartofIEEE Antennas and Wireless Propagation Letters-
dc.rightsIEEE Antennas and Wireless Propagation Letters. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectFinite difference methods-
dc.subjectTime-domain analysis-
dc.subjectComputational modeling-
dc.subjectNeurons-
dc.subjectData models-
dc.titleMachine-Learning-Based PML for the FDTD Method-
dc.typeArticle-
dc.identifier.emailJiang, L: jianglj@hku.hk-
dc.identifier.authorityJiang, L=rp01338-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/LAWP.2018.2885570-
dc.identifier.scopuseid_2-s2.0-85058183624-
dc.identifier.hkuros306188-
dc.identifier.volume18-
dc.identifier.issue1-
dc.identifier.spage192-
dc.identifier.epage196-
dc.publisher.placeUnited States-

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