Publications

Find below a list of all the publications by Prof. Dr. Schillings.

• CS, B. Sprungk, P. Wacker, On the Convergence of the Laplace Approximation and Noise-Level-Robustness of Laplace-based Monte Carlo Methods for Bayesian Inverse Problems, arXiv:1901.03958.
• S. Haijan, M. Hintermueller, CS, N. Strogies, A Bayesian approach for parameter identification in gas networks.

Preprints

• C. Schillings, B. Sprungk, P. Wacker, On the Convergence of the Laplace Approximation and Noise-Level-Robustness of Laplace-based Monte Carlo Methods for Bayesian Inverse Problems, arXiv:1901.03958.
• S. Haijan, M. Hintermueller, C. Schillings, N. Strogies, A Bayesian approach for parameter identification in gas networks.

Articles in Journals and Refereed Proceedings Volumes

• M. Oesting, M. Schlather and C. Schillings. Sampling Sup-Normalized Spectral Functions
  for Brown-Resnick Processes, Stat. 8:e228, 2019.
• D. Blömker, C. Schillings, P. Wacker and S. Weissmann. Well-posedness and convergence
  analysis of Kalman inversion with perturbed observations, Inverse Problems, 2019.
• D. Blömker, C. Schillings, P. Wacker, A strongly convergent numerical scheme from EnKF continuum analysis, SIAM J Numerical Analysis 56(4):2537-2562, 2018.
• R. Hiptmair, L. Scarabosio, C. Schillings, C. Schwab. Large deformation shape uncertainty quantification in acoustic scattering, ACOM 44(5), 2018.
• C. Schillings and A. Stuart, Convergence analysis of ensemble Kalman inversion:the linear, noisy case, Applicable Analysis, 2017.
• C. Schillings, A. M. Stuart. Analysis of the ensemble Kalman filter for inverse problems, SIAM J Numerical Analysis 55(3), 1264-1290, 2017.
• A. Litvinenko, D. Liu, C. Schillings and V. Schulz, Uncertainty quantification and treatment in aircraft design – comparison of approaches, SIAM/ASA J. Uncertainty Quantification 5–1, 2017.
• C. Schillings and C. Schwab. Scaling limits in computational Bayesian inversion, ESAIM: M2AN, 50 6,1825-1856, 2016.
• C. Schillings, M. Sunnaker, J. Stelling, C. Schwab. Efficient characterization of parametric uncertainty of complex (bio)chemical networks, PLOS Comp. Biol., 2015.
• C. Schillings and C. Schwab. Sparsity in Bayesian inversion of parametric operator equations, Inverse Problems 30(6) (2014) 065007.
• R. Gantner, C. Schillings and C. Schwab. Binned multilevel Monte Carlo for Bayesian inverse problems with large data, DD22 Proceedings Volume in LNCSE 2013.
• C. Schillings and V. Schulz. On the influence of robustness measures on shape optimization, Optimization and Engineering, pp. 1–40, 2014.
• C. Schillings and C. Schwab. Sparse, adaptive Smolyak quadratures for Bayesian inverse problems, Inverse Problems 29 (2014) 065011.
• M. Hansen, C. Schillings, and C. Schwab. Sparse approximation algorithms for high dimensional parametric initial value problems, Proceedings of the 5th International Conference on High Performance Scientific Computing 2012.
• C. Schillings, S. Schmidt and V. Schulz. Efficient shape optimization for certain and uncertain aerodynamic design, Computers and Fluids, Vol. 46, No. 1, pp. 78–87, 2011.
• A. Borzi, V. Schulz, C. Schillings and G. von Winckel. On the treatment of distributed uncertainties in PDE-constrained optimization, GAMM-Mitteilungen, Vol. 33, No. 2, pp. 230–246, 2010.
• V. Schulz and C. Schillings. On the nature and treatment of uncertainties in aerodynamic design, AIAA Journal, Vol. 47, No. 3, pp. 646–654, 2009.

Miscellaneous Publications

• C. Schillings. Analysis of the Ensemble Kalman Filter for Inverse Problems, Oberwolfach Report, 47, 2016.
• C. Schwab and C. Schillings. Sparse quadrature approach to Bayesian inverse problems, Oberwolfach Report, 39(1):2237–2240, 2013.
• C. Schillings. A note on sparse, adaptive Smolyak quadratures for Bayesian inverse problems, Oberwolfach Report, 10(1):239–293, 2013.
• V. Schulz and C. Schillings, Optimal aerodynamic design under uncertainty, Management and Minimisation of Uncertainties and Errors in Numerical Aerodynamics, B. Eisfeld, H. Barnewitz, W. Fritz, and F. Thiele, eds., Vol. 122 of Notes on Numerical Fluid Mechanics and Multidisciplinary Design, Springer Berlin Heidelberg, pp. 297–338, 2013.
• C. Schillings and V. Schulz. Optimal aerodynamic design under uncertainties, ECCOMAS CFD & Optimization, I.H. Tuncer, ed., ISBN 978-605-61427-4-1, No. 2011–049, 2011.
• V. Schulz and C. Schillings. Optimal aerodynamic design under shape uncertainties,Trierer Forschungsbericht, 09–5, 2009.
• C. Dülk and C. Schillings. Patent application on the topic of optimal control of a fuel cell component, 2006.

Theses

• C. Schillings. Optimal aerodynamic design under uncertainties, PhD Thesis, Universität Trier, 2011.

• C. Schillings. Modeling of a fuel cell component in Matlab/Simulink using parameter estimation and neuronal networks (in German), DiplomaThesis, 2006.