Research focus

• Shape optimization and interface identification
• High performance optimization algorithms
• Algorithmic scalability for PDE constrained optimization

Office hours

Teaching

Publications

Journal articles:

• M. Siebenborn and A. Vogel. A shape optimization algorithm for cellular composites. Submitted to Springer Computing and Visualization in Science (2019), arxiv.org/1904.03860.
• M. Siebenborn and J. Wagner. A Multigrid Preconditioner for Tensor Product Spline Smoothing. Submitted to Springer Journal of Scientific Computing (2019), arxiv:1901.00654.
• T. Etling, R. Herzog, and M. Siebenborn. Optimum Experimental Design for Interface Identification Problems. Accepted for publication in SIAM Journal on Scientific Computing (2019), arXiv:1808.05776.
• M. Siebenborn. A shape optimization algorithm for interface identification allowing topological changes. In: Journal of Optimization Theory and Applications 177(2) (2018), 306-328.
• M. Siebenborn and K. Welker. Algorithmic Aspects of Multigrid Methods for Optimization in Shape Spaces. In: SIAM Journal on Scientific Computing 39.6 (2017), B1156-B1177.
• V. Schulz, M. Siebenborn, and K. Welker. Efficient PDE constrained shape optimization based on Steklov-Poincaré-Type metrics. In: SIAM Journal on Optimization 26.4 (2016), pp. 2800-2819.
• L. Grasedyck, C. Löbbert, G. Wittum, A. Nägel, V. Schulz, M. Siebenborn, R. Krause, P. Benedusi, U. Küster, and B. Dick. Space and Time Parallel Multigrid for Optimization and Uncertainty Quantification in PDE Simulations. In: Software for Exascale Computing - SPPEXA 2013-2015. Ed. by H.-J. Bungartz, P. Neumann, and E. W. Nagel. Springer International Publishing, (2016), pp. 507-523.
• V. Schulz and M. Siebenborn. Computational comparison of surface metrics for PDE constrained shape optimization. In: Computational Methods in Applied Mathematics 16.3 (2016), pp. 485-496.
• A. Nägel, V. Schulz, M. Siebenborn, and G. Wittum. Scalable shape optimization methods for structured inverse modeling in 3D diffusive processes. In: Computing and Visualization in Science 17.2 (2015), pp. 79-88.
• V. Schulz, M. Siebenborn, and K. Welker. Structured Inverse Modeling in Parabolic Diffusion Problems. In: SIAM Journal on Control and Optimization 53.6 (2015), pp. 3319-3338.
• M. Siebenborn, V. Schulz, and S. Schmidt. A curved-element unstructured discontinuous Galerkin method on GPUs for the Euler equations. In: Computing and Visualization in Science 15.2 (2012), pp. 61-73.

Refereed proceedings:

• V. Schulz, M. Siebenborn, and K. Welker. PDE constrained shape optimization as optimization on shape manifolds. In: Geometric Science of Information. Ed. by F. Nielsen and F. Barbaresco. Vol. 9389. Lecture Notes in Computer Science. 2015.
• V. Schulz, M. Siebenborn, and K. Welker. Towards a Lagrange-Newton approach for PDE constrained shape optimization. In: New trends in shape optimization. International Series of Numerical Mathematics. Springer, 2015.
• M. Siebenborn and V. Schulz. GPU Accelerated Discontinuous Galerkin Methods for Euler Equations and Its Adjoint. In: Proceedings of the High Performance Computing Symposium HPC 13. San Diego, California: Society for Computer Simulation International, 2013, 3:1-3:7.

Other publications:

• M. Siebenborn. Discontinuous Galerkin approaches for HPC flow simulations on stream processors. PhD thesis. Trier University, Germany, 2014.

Software:

• MinFEM: A minimal finite element tool for demonstration and teaching. (github.com/msiebenborn/MinFEM.jl)
• MGSS: A multigrid spline smoothing toolbox for high dimensional data analysis. (github.com/SplineSmoothing/MGSS)
• MGSS-Grid: Efficient spline smoothing toolbox for high dimensional data on grids. (github.com/SplineSmoothing/MGSS_grid)

Short CV