Technische Universität München

After receiving her diploma in mathematics from TUM in 1997 ('summa cum laude'), Miriam Mehl completed her doctorate ('summa cum laude') in the interdisciplinary area of biological wastewater treatment in 2001. She finished her habilitation in the TUM Department of Computer Science in summer 2010, and is involved in several projects in the fields of computational fluid dynamics (CFD), simulation of fluid-structure interactions, and high-performance computing. Since 2002, she has been heading the CFD Group at the Department for Scientific Computing in Computer Science at TUM. This group works with a strong focus on numerically and hardware-efficient algorithms for PDE solvers on high-performance computing architectures.Miriam Mehl has been working in a part-time job since 2002 due to her two children (born 2001 and 2003).

1993-1997 Bayerische Begabtenfoerderung

More publications are available here.

H.-J. Bungartz, J. Benk, B. Gatzhammer, M. Mehl and T. Neckel: Partitioned Simulation of Fluid-Structure Interaction on Cartesian Grids. In H.-J. Bungartz, M. Mehl and M. Schäfer (ed.), Fluid-Structure Interaction - Modelling, Simulation, Optimisation, Part II of LNCSE. Springer, Berlin, Heidelberg, 2010. to appear.

A. Atanasov, H.-J. Bungartz, J. Frisch, M. Mehl, R.-P. Mundani, E. Rank and C. van Treek: Computational
Steering of Complex Flow Simulations. In S. Wagner, A. Bode and G. Wellein (ed.), High Performance Computing in Science and Engineering, Garching 2009. Springer, Berlin, Heidelberg, 2010 to appear.

H.-J. Bungartz, M. Mehl, T. Neckel and T. Weinzierl: The PDE framework Peano applied to fluid dynamics.
In Computational Mechanics. Springer, Berlin Heidelberg, 2009. published online.

H.-J. Bungartz, M. Mehl and C. Zenger: Computer Science and Fluid Mechanics - An Essential Cooperation. In E. Hirschel and E. Krause (ed.), 100 Volumes Notes on Numerical Fluid Mechanics and Multidisciplinary Design (NNFM) and 40 Years Numerical Fluid Mechanics, Volume 100 of Notes on Numerical Fluid Mechanics and Multidisciplinary Design, p. 437-450. Springer-Verlag, Berlin Heidelberg, 2009.

M. Brenk, H.-J. Bungartz, M. Mehl, I. L. Muntean, T. Neckel and T. Weinzierl: Numerical Simulation of Particle Transport in a Drift Ratchet. In C. Johnson, D. Keyes and U. Rüde (ed.), SIAM Journal of Scientific Computing, Volume 30(6), p. 277-2798, 2008.

M. Brenk, H.-J. Bungartz, M. Mehl, I. L. Muntean, T. Neckel and K. Daubner: An Eulerian Approach for Partitioned Fluid-structure Simulations on Cartesian Grids. In Computational Mechanics, Volume 43(1), p. 115-124. Springer-Verlag, Berlin Heidelberg, 2008.

F. Günther, M. Mehl, M. Pögl and C. Zenger: A cache-aware algorithm for PDEs on hierarchical dat a structures based on space-filling curves. In SIAM Journal on Scientific Computing, Volume 28(5), p. 163-1650, 2006.

M. Mehl, T. Weinzierl and C. Zenger: A cache-oblivious self-adaptive full multigrid method.
In R. D. Falgout (ed.), Numerical Linear Algebra with Applications, Volume 13(2-3), p. 275-291. Wiley Interscience, 2006.

M. Kuehn, M. Mehl, M. Hausner, H.-J. Bungartz and S. Wuertz: Time-resolved Study of Biofilm Architecture
and Transport Processes Using Experimental and Simulation Techniques: The Role of EPS.
In Water Science & Technology, Volume 43(6), p. 143-151. IWA Publishing, 2001.

H.-J. Bungartz, M. Kuehn, M. Mehl, M. Hausner and S. Wuertz: Fluid flow and transport in defined biofilms: Experiments and numerical simulations on a microscale. In Water Science & Technology, Volume 41(4-5), p. 331-338. IWA Publishing, June 2000.

Miriam Mehls research topics are in the field of Scientific Computing with a special focus on fluid dynamics related applications. The major aim is to combine mathematical and computer science methods in order to achieve numerically and hardware efficient simulation software. This concerns both the simulation program itself but also the embedding into an application context, that is preprocessing, geometry description, and grid generation one the input side and
data interpretation and visualisation on the output side but also the embedding in larger contexts such as coupled multi-physics simulations and interactive simulations (computational steering). Since solvers for partial differential equations are typically data-intensive applications, low memory requirements and efficient data access algorithms are of particular importance and not trivial to achieve for state-of-the art
numerical methods such as multigrid solvers on adaptive grids with their complex data dependencies. For multi-physics problems, the challenges are to find flexible and efficient tools for code coupling to be able to reuse existing software and to develop stable numerical coupling strategies that work even with black-box solvers for the single-physics components.