Research interests

The activities of the workgroup focus on all types of partial differential equations, including stochastic (partial) differential equations with random coefficients. The simulation of these problems requires an efficient calculation of stochastic processes and adaptive mesh refinement with the aid of posteriori error estimators. Particularly interesting in this regard are time dependent problems from the fields of physics and technology, such as computational micromagnetism (Landau-Lifshitz-Gilbert equation). Methods from high-dimensional quadrature and approximation are used.

Another research direction is the development and analysis of efficient training and data compression algorithms for machine learning.

PhD students Amanda Huber BSc

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Projektass.(FWF) Andrea Scaglioni MSc

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