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001441237 001__ 1441237
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001441237 020__ $$a9783030820404$$q(electronic bk.)
001441237 020__ $$a3030820408$$q(electronic bk.)
001441237 020__ $$z3030820394
001441237 020__ $$z9783030820398
001441237 0247_ $$a10.1007/978-3-030-82040-4$$2doi
001441237 035__ $$aSP(OCoLC)1287957555
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001441237 049__ $$aISEA
001441237 050_4 $$aQA274.23
001441237 08204 $$a519.2/2$$223
001441237 1001_ $$aMilʹshteĭn, G. N.$$q(Grigoriĭ Noĭkhovich),$$eauthor.
001441237 24510 $$aStochastic numerics for mathematical physics /$$cGrigori N. Milstein, Michael V. Tretyakov.
001441237 250__ $$aSecond edition.
001441237 260__ $$aCham, Switzerland :$$bSpringer,$$c2021.
001441237 300__ $$a1 online resource
001441237 336__ $$atext$$btxt$$2rdacontent
001441237 337__ $$acomputer$$bc$$2rdamedia
001441237 338__ $$aonline resource$$bcr$$2rdacarrier
001441237 347__ $$atext file
001441237 347__ $$bPDF
001441237 4901_ $$aScientific computation,$$x2198-2589
001441237 504__ $$aIncludes bibliographical references and index.
001441237 5050_ $$aMean-square Approximation for Stochastic Di
001441237 506__ $$aAccess limited to authorized users.
001441237 520__ $$aThis book is a substantially revised and expanded edition reflecting major developments in stochastic numerics since the first edition was published in 2004. The new topics, in particular, include mean-square and weak approximations in the case of nonglobally Lipschitz coefficients of Stochastic Differential Equations (SDEs) including the concept of rejecting trajectories; conditional probabilistic representations and their application to practical variance reduction using regression methods; multi-level Monte Carlo method; computing ergodic limits and additional classes of geometric integrators used in molecular dynamics; numerical methods for FBSDEs; approximation of parabolic SPDEs and nonlinear filtering problem based on the method of characteristics. SDEs have many applications in the natural sciences and in finance. Besides, the employment of probabilistic representations together with the Monte Carlo technique allows us to reduce the solution of multi-dimensional problems for partial differential equations to the integration of stochastic equations. This approach leads to powerful computational mathematics that is presented in the treatise. Many special schemes for SDEs are presented. In the second part of the book numerical methods for solving complicated problems for partial differential equations occurring in practical applications, both linear and nonlinear, are constructed. All the methods are presented with proofs and hence founded on rigorous reasoning, thus giving the book textbook potential. An overwhelming majority of the methods are accompanied by the corresponding numerical algorithms which are ready for implementation in practice. The book addresses researchers and graduate students in numerical analysis, applied probability, physics, chemistry, and engineering as well as mathematical biology and financial mathematics.
001441237 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 13, 2021).
001441237 650_0 $$aStochastic differential equations.
001441237 650_0 $$aDifferential equations, Partial$$xNumerical solutions.
001441237 650_0 $$aMathematical physics.
001441237 650_6 $$aÉquations différentielles stochastiques.
001441237 650_6 $$aÉquations aux dérivées partielles$$xSolutions numériques.
001441237 650_6 $$aPhysique mathématique.
001441237 655_0 $$aElectronic books.
001441237 7001_ $$aTretyakov, Michael V.,$$eauthor.
001441237 77608 $$iPrint version: $$z3030820394$$z9783030820398$$w(OCoLC)1257889412
001441237 830_0 $$aScientific computation,$$x2198-2589
001441237 852__ $$bebk
001441237 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-82040-4$$zOnline Access$$91397441.1
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001441237 980__ $$aBIB
001441237 980__ $$aEBOOK
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