Item Infomation
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | KaraosmanoÄŸlu, B. | vi |
dc.contributor.other | Ergül, Özgür | vi |
dc.date.accessioned | 2020-11-26T09:07:55Z | - |
dc.date.available | 2020-11-26T09:07:55Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE Antennas and Wireless Propagation Letters, (2019), VOL. 18, NO. 11, pp 2264-2266 | vi |
dc.identifier.uri | http://tailieuso.tlu.edu.vn/handle/DHTL/9796 | - |
dc.description.abstract | In this letter, we present a novel approach based on using convolutional neural networks (CNNs) to visually predict solutions of electromagnetic problems. CNN models are constructed and trained such that images of surface currents obtained at the early stages of an iterative solution can be used to predict images of the final (converged) solution. Numerical experiments demonstrate that the predicted images contain significantly better visual details than the corresponding input images. The developed approach and the constructed CNN models can provide visual information on the solution of a given problem using only a few iterations without performing the whole iterative solution. | vi |
dc.language | en_US | vi |
dc.publisher | IEEE Xplore | vi |
dc.subject | Integral equations | vi |
dc.subject | method of moments | vi |
dc.subject | multilevel fast multipole algorithm (MLFMA) | vi |
dc.subject | machine learning algorithms. | vi |
dc.title | Visual Result Prediction in Electromagnetic Simulations Using Machine Learning | vi |
dc.type | BB | vi |
Appears in Collections: | Tài liệu hỗ trợ nghiên cứu khoa học |
Files in This Item:
Bạn đọc là cán bộ, giáo viên, sinh viên của Trường Đại học Thuỷ Lợi cần đăng nhập để Xem trực tuyến/Tải về
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.