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  • Produktbild: Kolmogorov Equations for Stochastic PDEs
  • Produktbild: Kolmogorov Equations for Stochastic PDEs

Kolmogorov Equations for Stochastic PDEs

Aus der Reihe Advanced Courses in Mathematics - CRM Barcelona

39,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

15.12.2004

Abbildungen

VII, 182 p.

Verlag

Springer Basel

Seitenzahl

182

Maße (L/B/H)

23,5/15,5/1,1 cm

Gewicht

306 g

Auflage

2004

Sprache

Englisch

ISBN

978-3-7643-7216-3

Beschreibung

Rezension

Many of the results presented here are appearing in book form for the first time. (...) The writing style is clear. Needless to say, the level of mathematics is high and will no doubt tax the average mathematics and physics graduate student. For the devoted student, however, this book offers an excellent basis for a 1-year course on the subject. It is definitely recommended.


JASA Reviews

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

15.12.2004

Abbildungen

VII, 182 p.

Verlag

Springer Basel

Seitenzahl

182

Maße (L/B/H)

23,5/15,5/1,1 cm

Gewicht

306 g

Auflage

2004

Sprache

Englisch

ISBN

978-3-7643-7216-3

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Kolmogorov Equations for Stochastic PDEs
  • Produktbild: Kolmogorov Equations for Stochastic PDEs
  • 1 Introduction and Preliminaries.- 1.1 Introduction.- 1.2 Preliminaries ix.- 2 Stochastic Perturbations of Linear Equations.- 2.1 Introduction.- 2.2 The stochastic convolution.- 2.3 The Ornstein—Uhlenbeck semigroup Rt.- 2.4 The case when Rt is strong Feller.- 2.5 Asymptotic behaviour of solutions, invariant measures.- 2.6 The transition semigroup in Lp(H, ?).- 2.7 Poincaré and log-Sobolev inequalities.- 2.8 Some complements.- 3 Stochastic Differential Equations with Lipschitz Nonlinearities.- 3.1 Introduction and setting of the problem.- 3.2 Existence, uniqueness and approximation.- 3.3 The transition semigroup.- 3.4 Invariant measure v.- 3.5 The transition semigroup in L2 (H, v).- 3.6 The integration by parts formula and its consequences.- 3.7 Comparison of v with a Gaussian measure.- 4 Reaction-Diffusion Equations.- 4.1 Introduction and setting of the problem.- 4.2 Solution of the stochastic differential equation.- 4.3 Feller and strong Feller properties.- 4.4 Irreducibility.- 4.5 Existence of invariant measure.- 4.6 The transition semigroup in L2 (H, v).- 4.7 The integration by parts formula and its consequences.- 4.8 Comparison of v with a Gaussian measure.- 4.9 Compactness of the embedding W1,2 (H, v) ? L2 (H, v).- 4.10 Gradient systems.- 5 The Stochastic Burgers Equation.- 5.1 Introduction and preliminaries.- 5.2 Solution of the stochastic differential equation.- 5.3 Estimates for the solutions.- 5.4 Estimates for the derivative of the solution w.r.t. the initial datum.- 5.5 Strong Feller property and irreducibility.- 5.6 Invariant measure v.- 5.6.1 Estimate of some integral with respect to v.- 5.7 Kolmogorov equation.- 6 The Stochastic 2D Navier—Stokes Equation.- 6.1 Introduction and preliminaries.- 6.2 Solution of the stochastic equation.- 6.3 Estimatesfor the solution.- 6.4 Invariant measure v.- 6.5 Kolmogorov equation.