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- Publisher Website: 10.1109/LAWP.2018.2885570
- Scopus: eid_2-s2.0-85058183624
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Article: Machine-Learning-Based PML for the FDTD Method
Title | Machine-Learning-Based PML for the FDTD Method |
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Authors | |
Keywords | Finite difference methods Time-domain analysis Computational modeling Neurons Data models |
Issue Date | 2019 |
Publisher | Institute 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? |
Abstract | In 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 Identifier | http://hdl.handle.net/10722/278141 |
ISSN | 2023 Impact Factor: 3.7 2023 SCImago Journal Rankings: 1.634 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | YAO, H | - |
dc.contributor.author | Jiang, L | - |
dc.date.accessioned | 2019-10-04T08:08:17Z | - |
dc.date.available | 2019-10-04T08:08:17Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE Antennas and Wireless Propagation Letters, 2019, v. 18 n. 1, p. 192-196 | - |
dc.identifier.issn | 1536-1225 | - |
dc.identifier.uri | http://hdl.handle.net/10722/278141 | - |
dc.description.abstract | In 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.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7727 | - |
dc.relation.ispartof | IEEE Antennas and Wireless Propagation Letters | - |
dc.rights | IEEE 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.subject | Finite difference methods | - |
dc.subject | Time-domain analysis | - |
dc.subject | Computational modeling | - |
dc.subject | Neurons | - |
dc.subject | Data models | - |
dc.title | Machine-Learning-Based PML for the FDTD Method | - |
dc.type | Article | - |
dc.identifier.email | Jiang, L: jianglj@hku.hk | - |
dc.identifier.authority | Jiang, L=rp01338 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/LAWP.2018.2885570 | - |
dc.identifier.scopus | eid_2-s2.0-85058183624 | - |
dc.identifier.hkuros | 306188 | - |
dc.identifier.volume | 18 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 192 | - |
dc.identifier.epage | 196 | - |
dc.identifier.isi | WOS:000455707900040 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1536-1225 | - |