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| In this talk, we consider controlled linear stochastic evolution equations with Levy noise. To solve such systems numerically, finite dimensional approximations are needed. So, we apply a Galerkin scheme leading to a sequence of ordinary linear stochastic differential equations. In order to obtain a good approximation the Galerkin solution can be of high dimension. To reduce the high dimension for practical computations we consider model order reduction techniques. In this talk, we describe a particular model order reduction technique, provide an error bound for the estimation, and show some numerical results for a particular example. |
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