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001			10.1002/9781119989271
003			Wiley
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020			_a9781119989271 _qelectronic
024	7		_a10.1002/9781119989271 _2doi
041			_aeng
100	1		_aPoljak, Dragan _4aut _992300
245	1	0	_aDeterministic and Stochastic Modeling in Computational Electromagnetics _bIntegral and Differential Equation Approaches _h[electronic resource] _cDragan Poljak, Anna Šušnjara
250			_a1
260			_bWiley _c2023
264		1	_bWiley _c2023
300			_a576pp.;
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _2rdaft
347			_bPDF
520			_aDeterministic and Stochastic Modeling in Computational Electromagnetics Help protect your network with this important reference work on cyber security Deterministic computational models are those for which all inputs are precisely known, whereas stochastic modeling reflects uncertainty or randomness in one or more of the data inputs. Many problems in computational engineering therefore require both deterministic and stochastic modeling to be used in parallel, allowing for different degrees of confidence and incorporating datasets of different kinds. In particular, non-intrusive stochastic methods can be easily combined with widely used deterministic approaches, enabling this more robust form of data analysis to be applied to a range of computational challenges. Deterministic and Stochastic Modeling in Computational Electromagnetics provides a rare treatment of parallel deterministic-stochastic computational modeling and its beneficial applications. Unlike other works of its kind, which generally treat deterministic and stochastic modeling in isolation from one another, it aims to demonstrate the usefulness of a combined approach and present particular use-cases in which such an approach is clearly required. It offers a non-intrusive stochastic approach which can be incorporated with minimal effort into virtually all existing computational models. Readers will also find: A range of specific examples demonstrating the efficiency of deterministic-stochastic modeling Computational examples of successful applications including ground penetrating radars (GPR), radiation from 5G systems, transcranial magnetic and electric stimulation (TMS and TES), and more Introduction to fundamental principles in field theory to ground the discussion of computational modeling Deterministic and Stochastic Modeling in Computational Electromagnetics is a valuable reference for researchers, including graduate and undergraduate students, in computational electromagnetics, as well as to multidisciplinary researchers, engineers, physicists, and mathematicians.
650		4	_aElectromagnetic Theory _991431
650		4	_aComputational / Numerical Methods
650		4	_aRF / Microwave Theory & Techniques _991221
653	0	0	_aComputational modeling computer engineering; CEM; data analysis; data modeling; electrical engineering; ground penetrating radars; GPR; grounding systems; 5G system; transcranial magnetic simulation; TMS; transcranial electrical simulation; ILS
700	1		_aŠušnjara, Anna _4aut _992301
776	0	8	_iPrinted edition: _z9781119989240
856	4	0	_uhttps://doi.org/10.1002/9781119989271 _aWiley Online Library
856	4	0	_uhttps://onlinelibrary.wiley.com/action/showBook?doi=10.1002%2F9781119989271 _aWiley Online Library
856	4	2	_3Cover Image _uhttps://onlinelibrary.wiley.com/cms/asset/1f2d50c5-07f5-4d45-98d0-398b22388dea/9781119989271.cover.gif _aWiley Online Library
999			_c67296 _d67296