• Uncertainty Quantification
• Stochastic Galerkin Methods
• Finite element methods
• Adaptivity
• Iterative solvers & preconditioners for linear systems of equations
• Reduced order models

Publications

• S. Ullmann, C. Müller, J. Lang. Stochastic Galerkin reduced basis methods for parametrized linear convection-diffusion-reaction-equations. Fluids, Special Issue Reduced Order Models for Computational Fluid Dynamics, 6(8), 263, 2021.
• C. Müller, S. Ullmann, J. Lang. A Bramble-Pasciak conjugate gradient method for discrete Stokes equations with random viscosity. SIAM/ASA J. Uncertainty Quantification, 7(3), 787–805, 2019. [arXiv]
• C. Müller, S. Ullmann, J. Lang. A Bramble-Pasciak conjugate gradient method for discrete Stokes equations with lognormal random viscosity. In Recent Advances in Computational Engineering, ICCE 2017, M. Schäfer, M. Behr, M. Mehrl, and B. Wohlmuth, eds., vol. 124 of Lect. Notes Comput. Sci. Eng., Springer, 2018. [arXiv]
• M. Kronbichler, S. Schoeder, C. Müller, W. A. Wall. Comparison of implicit and explicit hybridizable discontinuous Galerkin methods for the acoustic wave equation. Int. J. Numer. Meth. Eng., 106(9), 712-739, 2015.