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001442467 001__ 1442467
001442467 003__ OCoLC
001442467 005__ 20230310003420.0
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001442467 020__ $$a9789811662614$$q(electronic bk.)
001442467 020__ $$a9811662614$$q(electronic bk.)
001442467 020__ $$z9811662606
001442467 020__ $$z9789811662607
001442467 0247_ $$a10.1007/978-981-16-6261-4$$2doi
001442467 035__ $$aSP(OCoLC)1280102591
001442467 040__ $$aYDX$$beng$$epn$$cYDX$$dGW5XE$$dEBLCP$$dDCT$$dOCLCF$$dOCLCO$$dDKU$$dOCLCO$$dOCLCQ$$dCOM$$dOCLCQ
001442467 049__ $$aISEA
001442467 050_4 $$aQC665.S3
001442467 08204 $$a539.2$$223
001442467 1001_ $$aRen, Qiang,$$eauthor.
001442467 24510 $$aSophisticated electromagnetic forward scattering solver via deep learning /$$cQiang Ren, Yinpeng Wang, Yongzhong Li, Shutong Qi.
001442467 260__ $$aSingapore :$$bSpringer,$$c[2022]
001442467 300__ $$a1 online resource
001442467 336__ $$atext$$btxt$$2rdacontent
001442467 337__ $$acomputer$$bc$$2rdamedia
001442467 338__ $$aonline resource$$bcr$$2rdacarrier
001442467 347__ $$atext file
001442467 347__ $$bPDF
001442467 504__ $$aIncludes bibliographical references and index.
001442467 5050_ $$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.
001442467 506__ $$aAccess limited to authorized users.
001442467 520__ $$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.
001442467 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 4, 2021).
001442467 650_0 $$aElectromagnetic waves$$xScattering$$xData processing.
001442467 650_0 $$aMachine learning.
001442467 650_6 $$aOndes électromagnétiques$$xDiffusion$$xInformatique.
001442467 650_6 $$aApprentissage automatique.
001442467 655_0 $$aElectronic books.
001442467 7001_ $$aWang, Yinpeng,$$eauthor.
001442467 7001_ $$aLi, Yongzhong,$$eauthor.
001442467 7001_ $$aQi, Shutong,$$eauthor.
001442467 77608 $$iPrint version:$$aRen, Qiang.$$tSophisticated electromagnetic forward scattering solver via deep learning.$$dSingapore : Springer, [2022]$$z9811662606$$z9789811662607$$w(OCoLC)1264141100
001442467 852__ $$bebk
001442467 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-6261-4$$zOnline Access$$91397441.1
001442467 909CO $$ooai:library.usi.edu:1442467$$pGLOBAL_SET
001442467 980__ $$aBIB
001442467 980__ $$aEBOOK
001442467 982__ $$aEbook
001442467 983__ $$aOnline
001442467 994__ $$a92$$bISE