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Juergen Geiser
Juergen Geiser
Associate Professor
Juergen Geiser has an interdisciplinary education based on a mathematician, engineer and physicist. He started his education as a mathematician at University of Stuttgart and finishing his doctoral studies at University of Heidelberg in the field of scientific computing in the field of hydrodynamics and numerical methods of PDEs. In the following years, he started his post-doc positions at the Weierstrass Institute and Humboldt University of Berlin, where he analysed and simulated multiphase problems of coupled PDEs, which are applied in thin film deposition processes. Later, he obtained a research position at University of Greifswald in the field of plasma dynamics and simulations of multiscale and multicomponent problems. He obtained his habilitation and venia legendi in the field of computational engineering in 2012/2013 at Ruhr-University Bochum, where he modelled and analyse plasma-dynamical problems. Since then, he has holding regular lectures and for his innovative “Inverted Classroom”-concept, PD Dr. Jürgen Geiser was awarded with the eLearning award in 2016. In 2015 and 2016, he received a visiting professor position at the Centrale Supelec, Chatenay-Malabry, France, in the field of numerical analysis. In 2017 and 2018, he received a short-term reader position at the Imperial College London and at the University of Warwick, UK, in the field of numerical analysis. Since 2019, he is involved in a particle transport project at the University of Luxembourg.

Public Documents 1
Iterative Splitting Methods for Stochastic Differential Equations: Theory and Applica...
Juergen Geiser

Juergen Geiser

October 12, 2020
In this paper, we present splitting methods that are based on iterative schemes for stochastic differential equations. The methods are applied to plasma simulations. The motivation arose from solving problems involving Coulomb collisions, which are modeled by nonlinear stochastic differential equations. We apply Langevin equations to model these collisions and we obtain coupled nonlinear stochastic differential equations, which are difficult to solve. We propose stochastic splitting schemes that generalise well-known deterministic splitting schemes. The benefit of decomposing the equations into different parts and solving each part individually is taken into account in the analysis of the new iterative splitting schemes. The increase in the convergence order of the iterative splitting scheme with the number of iteration steps is an important and valuable property. The numerical analysis and applications to various problems involving Coulomb collisions in plasma applications are presented, and show the benefits of the iterative splitting schemes.

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