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008			211019s2022 si | s |||| 0|eng d
020			_a9789811662614
024	7		_a10.1007/978-981-16-6261-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQC661 _b2022 EB
100	1		_aRen, Qiang _eautor _9687328 _c(Associate professor)
245	1	0	_aSophisticated Electromagnetic Forward Scattering Solver via Deep Learning _cby Qiang Ren, Yinpeng Wang, Yongzhong Li, Shutong Qi
250			_a1st edition 2022
264		1	_aSingapore _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XVIII, 125 páginas) _b106 ilustraciones, 90 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_aIntroduction to Electromagnetic Problems -- Basic Principles of Unveiling Electromagnetic Problems Based on Deep Learning -- Building Database -- Two-Dimensional Electromagnetic Scattering Solver -- Three-Dimensional Electromagnetic Scattering Solver.
520			_aThis book investigates in detail the deep learning (DL) techniques in electromagnetic (EM) near-field scattering problems, assessing its potential to replace traditional numerical solvers in real-time forecast scenarios. Studies on EM scattering problems have attracted researchers in various fields, such as antenna design, geophysical exploration and remote sensing. Pursuing a holistic perspective, the book introduces the whole workflow in utilizing the DL framework to solve the scattering problems. To achieve precise approximation, medium-scale data sets are sufficient in training the proposed model. As a result, the fully trained framework can realize three orders of magnitude faster than the conventional FDFD solver. It is worth noting that the 2D and 3D scatterers in the scheme can be either lossless medium or metal, allowing the model to be more applicable. This book is intended for graduate students who are interested in deep learning with computational electromagnetics, professional practitioners working on EM scattering, or other corresponding researchers.
988			_aSpringer_Engineering_2022
650		7	_2embne _9138716 _aOndas electromagnéticas
776	0	8	_iPrinted edition: _z9789811662607
776	0	8	_iPrinted edition: _z9789811662621
776	0	8	_iPrinted edition: _z9789811662638
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-6261-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eu _zSI