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Claudine von Hallern: On the Numerical Approximation of Stochastic Partial Differential Equations

Time: Wed 2021-03-24 14.00 - 14.45

Location: Zoom, meeting ID: 61133297865

Participating: Claudine von Hallern, Kiel University

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Abstract

Stochastic partial differential equations are a powerful tool in modeling various phenomena from finance to the ocean sciences. Since analytical solutions to these equations are, in general,not computable, there is a high demand for numerical schemes to approximate these processes. In this talk,we give a short introduction to stochastic partial differential equations and illustrate the characteristics of numerical schemes for this type of equation. The solution process belongs to a Hilbert space of infinite dimension, so the approximation requires a discretization in space and time and an approximation of the driving stochastic process. We focus on derivative-free methods that maintain the same high order of convergence compared to schemes that include the derivative of the diffusion operator but at the same time involve a reduced computational cost.