000			03141nam a22004335i 4500
001			u376055
003			SIRSI
005			20160812084354.0
007			cr nn 008mamaa
008			110701s2011 gw | s |||| 0|eng d
020			_a9783642203114 _9978-3-642-20311-4
040			_cMX-MeUAM
050		4	_aQ342
082	0	4	_a006.3 _223
100	1		_aHeinz, Stefan. _eauthor.
245	1	0	_aMathematical Modeling _h[recurso electrónico] / _cby Stefan Heinz.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011.
300			_aXVI, 460 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
520			_aThe whole picture of Mathematical Modeling is systematically and thoroughly explained in this text for undergraduate and graduate students of mathematics, engineering, economics, finance, biology, chemistry, and physics. This textbook gives an overview of the spectrum of modeling techniques, deterministic and stochastic methods, and first-principle and empirical solutions. Complete range: The text continuously covers the complete range of basic modeling techniques: it provides a consistent transition from simple algebraic analysis methods to simulation methods used for research. Such an overview of the spectrum of modeling techniques is very helpful for the understanding of how a research problem considered can be appropriately addressed. Complete methods: Real-world processes always involve uncertainty, and the consideration of randomness is often relevant. Many students know deterministic methods, but they do hardly have access to stochastic methods, which are described in advanced textbooks on probability theory. The book develops consistently both deterministic and stochastic methods. In particular, it shows how deterministic methods are generalized by stochastic methods. Complete solutions: A variety of empirical approximations is often available for the modeling of processes. The question of which assumption is valid under certain conditions is clearly relevant. The book provides a bridge between empirical modeling and first-principle methods: it explains how the principles of modeling can be used to explain the validity of empirical assumptions. The basic features of micro-scale and macro-scale modeling are discussed – which is an important problem of current research.
650		0	_aEngineering.
650		0	_aChemistry _xMathematics.
650		0	_aMathematical physics.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aMathematical Methods in Physics.
650	2	4	_aMathematical Modeling and Industrial Mathematics.
650	2	4	_aMath. Applications in Chemistry.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642203107
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-20311-4
596			_a19
942			_cLIBRO_ELEC
999			_c203935 _d203935