Approximation and simulation of Lévy-driven SPDE

​Numerical analysis of stochastic partial differential equations is a quite young and very active area of research. Since analytical solutions of these equations are only rarely available, approximation of sample paths, moments, or probabilities is necessary. The quantity of interest depends on the type of application, e.g., finance, engineering, or filtering. The goal of the project is to answer some current open questions in the research area with methods from stochastic analysis, numerical analysis, and mathematical statistics. The research questions are related to different definitions of consistency of approximation schemes, the Lax equivalence theorem, weak convergence results using Malliavin calculus, construction of efficient algorithms for random fields, statistical properties of multilevel Monte Carlo algorithms, and mean-square stability regions.


Annika Lang (contact)

Docent at Mathematical Sciences, Mathematical Statistics

Adam Andersson

Forskare at Mathematical Sciences, Mathematics

David Bolin

Universitetslektor at Mathematical Sciences, Mathematical Statistics

Stig Larsson

at Mathematical Sciences, Mathematics

Andreas Petersson

at Mathematical Sciences, Mathematical Statistics


Johannes Kepler University of Linz (JKU)

Linz, Austria

Technische Universität Berlin

Berlin, Germany


Swedish Research Council (VR)

Funding years 2015–2018

More information

Project Web Page at Chalmers

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