Strong and Weak Approximation of Semilinear Stochastic Evolution Equations

Raphael Kruse

ISBN 10: 331902230X / ISBN 13: 9783319022307

Verlag: Springer International Publishing, Springer International Publishing, 2013

Beschreibung

Druck auf Anfrage Neuware - Printed after ordering - In this book we analyze the error caused by numerical schemes for the approximation of semilinear stochastic evolution equations (SEEq) in a Hilbert space-valued setting. The numerical schemes considered combine Galerkin finite element methods with Euler-type temporal approximations. Starting from a precise analysis of the spatio-temporal regularity of the mild solution to the SEEq, we derive and prove optimal error estimates of the strong error of convergence in the first part of the book.The second part deals with a new approach to the so-called weak error of convergence, which measures the distance between the law of the numerical solution and the law of the exact solution. This approach is based on Bismut's integration by parts formula and the Malliavin calculus for infinite dimensional stochastic processes. These techniques are developed and explained in a separate chapter, before the weak convergence is proven for linear SEEq.

Bestandsnummer des Verkäufers 9783319022307

Bibliografische Details

• Titel: Strong and Weak Approximation of Semilinear Stochastic Evolution Equations
• Verlag: Springer International Publishing, Springer International Publishing
• Erscheinungsjahr: 2013
• Sprache: Englisch
• ISBN-10: 331902230X
• ISBN-13: 9783319022307
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 235B x 155 H x 11 T Millimeter Breite: 235 mm, Höhe: 155 mm, Tiefe: 11 mm
• Gewicht: 306 Gramm

Über diesen Titel

Inhaltsangabe

In this book we analyze the error caused by numerical schemes for the approximation of semilinear stochastic evolution equations (SEEq) in a Hilbert space-valued setting. The numerical schemes considered combine Galerkin finite element methods with Euler-type temporal approximations. Starting from a precise analysis of the spatio-temporal regularity of the mild solution to the SEEq, we derive and prove optimal error estimates of the strong error of convergence in the first part of the book.

The second part deals with a new approach to the so-called weak error of convergence, which measures the distance between the law of the numerical solution and the law of the exact solution. This approach is based on Bismut’s integration by parts formula and the Malliavin calculus for infinite dimensional stochastic processes. These techniques are developed and explained in a separate chapter, before the weak convergence is proven for linear SEEq.