Computational methods for stochastic differential equations

Education facts

Code: MMA630

Modeling under uncertainty has become one of the buzzwords of these days. Finance, weather prediction, biology, and geophysics are just some examples where we can nowadays apply random models. To use these models, we have to understand which information is required from the model in practice and how it can be extracted efficiently. Typical information that needs to be computed is so called "quantities of interest" which are of the form E[g(X)], where X is the solution to a stochastic differential equation given by the random model, g is some functional, and E notes the expected value. In this course we discuss the efficient simulation of such quantities from two perspectives: As a first approach, we consider approximations of X and combine them with Monte Carlo methods to approximate the expected value. Secondly, we observe that our quantity of interest satisfies a partial differential equation, which we discretize with finite element methods. A combination of theory and explicit implementation of examples from applications helps us to get a sense of the power of the two different approaches.

Entry requirements

General entry requirements and the equivalent of the courses MSA350 Stochastic Calculus and MMG800 Partial Differential Equations.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Computational methods for stochastic differential equations

  University of Gothenburg

  Start date: 19 January 2026

  End date: 22 March 2026

  Pace of study: 50 %