Thesis for the Degree of Licentiate of Philosophy Variational Methods for Moments of Solutions to Stochastic Differential Equations Kristin Kirchner Division of Applied Mathematics and Statistics Department of Mathematical Sciences Chalmers University of Technology and University of Gothenburg Gothenburg, Sweden, 2016 Variational Methods for Moments of Solutions to Stochastic Differential Equations Kristin Kirchner c Kristin Kirchner, 2016. Department of Mathematical Sciences Chalmers University of Technology and University of Gothenburg SE-412 96 Gothenburg, Sweden Phone: +46 (0)31 772 53 57 Author e-mail:
[email protected] Printed in Gothenburg, Sweden 2016 Variational Methods for Moments of Solutions to Stochastic Differential Equations Kristin Kirchner Department of Mathematical Sciences Chalmers University of Technology and University of Gothenburg Abstract Numerical methods for stochastic differential equations typically estimate moments of the solution from sampled paths. Instead, we pursue the approach proposed by A. Lang, S. Larsson, and Ch. Schwab1, who derived well-posed deterministic, tensorized evolution equations for the second moment and the covariance of the solution to a parabolic stochastic partial differential equation driven by additive Wiener noise. In Paper I we consider parabolic stochastic partial differential equations with multiplicative L´evy noise of affine type. For the second moment of the mild solution, a deterministic space-time variational problem is derived. It is posed on projective and injective tensor product spaces as trial and test spaces. Well- posedness is proven under appropriate assumptions on the noise term. From these results, a deterministic equation for the covariance function is deduced. These deterministic equations in variational form are used in Paper II to derive numerical methods for approximating the first and second moment of the solution to a stochastic ordinary differential equation driven by additive or multiplicative Wiener noise.