N. Kühl, J. Kröger, M. Siebenborn, M. Hinze, and T. Rung. Adjoint Complement to the Volume-of-Fluid Method for Immiscible Flows. Submitted to: Journal of Computational Physics (2020), arXiv:2009.03957.
S. Onyshkevych and M. Siebenborn. Mesh quality preserving shape optimization using nonlinear extension operators. Submitted to: Journal of Optimization Theory and Applications (2020), arXiv:2006.04420.
J. Haubner, M. Siebenborn, and M. Ulbrich. A Continuous Perspective on Shape Optimization Via Domain Transformations. Accepted for publication: SIAM Journal on Scientific Computing (2020), arXiv:2004.06942.
J. Pinzon, M. Siebenborn, and A. Vogel. Parallel 3d shape optimization for cellular composites on large distributed-memory clusters. Journal of Advanced Simulation in Science and Engineering 7.1 (2020), pp. 117–135, arXiv:2003.09683.
M. Siebenborn and J. Wagner. A Multigrid Preconditioner for Tensor Product Spline Smoothing. Submitted to: Springer Computational Statistics (2019), arXiv:1901.00654.
M. Siebenborn and A. Vogel. A shape optimization algorithm for cellular composites. To appear in: Springer Computing and Visualization in Science (2019), arXiv:1904.03860.
T. Etling, R. Herzog, and M. Siebenborn. Optimum Experimental Design for Interface Identification Problems. SIAM Journal on Scientific Computing 41.6 (2019).
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-Poincare-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.