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005			20231122141146.0
007			cr nn 008mamaa
008			211019s2022 si | s |||| 0|eng d
020			_a9789811662614
024	7		_a10.1007/978-981-16-6261-4 _2doi
040			_aTR-AnTOB _beng _cTR-AnTOB _erda
041			_aeng
050		4	_aQC665.S3
072		7	_aTJF _2bicssc
072		7	_aTEC024000 _2bisacsh
072		7	_aTJF _2thema
090			_aQC665.S3EBK
100	1		_aRen, Qiang. _eauthor. _0(orcid)0000-0002-2581-7709 _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aSophisticated Electromagnetic Forward Scattering Solver via Deep Learning _h[electronic resource] / _cby Qiang Ren, Yinpeng Wang, Yongzhong Li, Shutong Qi.
250			_a1st ed. 2022.
264		1	_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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.
650		0	_aTelecommunication.
650		0	_aMathematics—Data processing.
650		0	_aMathematical physics.
650		0	_aComputer simulation.
650		0	_aMachine learning.
650		0	_aArtificial intelligence.
650	1	4	_aMicrowaves, RF Engineering and Optical Communications.
650	2	4	_aComputational Science and Engineering.
650	2	4	_aComputational Physics and Simulations.
650	2	4	_aMachine Learning.
650	2	4	_aArtificial Intelligence.
653		0	_aElectromagnetic waves -- Scattering -- Data processing
653		0	_aMachine learning
700	1		_aWang, Yinpeng. _eauthor. _0(orcid)0000-0002-8293-6499 _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aLi, Yongzhong. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aQi, Shutong. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-981-16-6261-4 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK