Strong and Weak Approximation of Semilinear Stochastic Evolution Equations
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The work Strong and Weak Approximation of Semilinear Stochastic Evolution Equations represents a distinct intellectual or artistic creation found in Boston University Libraries. This resource is a combination of several types including: Work, Language Material, Books.
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Strong and Weak Approximation of Semilinear Stochastic Evolution Equations
Resource Information
The work Strong and Weak Approximation of Semilinear Stochastic Evolution Equations represents a distinct intellectual or artistic creation found in Boston University Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 Strong and Weak Approximation of Semilinear Stochastic Evolution Equations
 Statement of responsibility
 by Raphael Kruse
 Subject

 Distribution (Probability theory)
 Probability Theory and Stochastic Processes
 Numerical Analysis
 Differential equations, partial
 Distribution (Probability theory)
 Numerical analysis
 Electronic resources
 Numerical analysis
 Mathematics
 Partial Differential Equations
 Mathematics
 Numerical Analysis
 Distribution (Probability theory)
 Differential equations, partial
 Language
 eng
 Summary
 In this book we analyze the error caused by numerical schemes for the approximation of semilinear stochastic evolution equations (SEEq) in a Hilbert spacevalued setting. The numerical schemes considered combine Galerkin finite element methods with Eulertype temporal approximations. Starting from a precise analysis of the spatiotemporal 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 socalled 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
 Image bit depth
 0
 LC call number
 QA297299.4
 Literary form
 non fiction
 Series statement
 Lecture Notes in Mathematics,
 Series volume
 2093
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