C. Schillings

发表

On the influence of robustness measures on shape optimization with stochastic uncertainties

V. Schulz, C. Schillings, 2014, Optimization and Engineering.

Convergence Analysis of the Ensemble Kalman Filter for Inverse Problems: the Noisy Case

A. Stuart, C. Schillings, 2017 .

Ensemble-based gradient inference for particle methods in optimization and sampling

C. Totzeck, Philipp Wacker, C. Schillings, 2022, ArXiv.

Large deformation shape uncertainty quantification in acoustic scattering

R. Hiptmair, C. Schwab, L. Scarabosio, 2018, Advances in Computational Mathematics.

Scaling limits in computational Bayesian inversion

C. Schwab, Claudia Schillings, C. Schillings, 2014 .

A strongly convergent numerical scheme from EnKF continuum analysis

D. Blömker, Philipp Wacker, C. Schillings, 2017 .

Analysis of the Ensemble Kalman Filter for Inverse Problems

Andrew M. Stuart, Claudia Schillings, A. Stuart, 2016, SIAM J. Numer. Anal..

Bayes Linear Methods for Inverse Problems

J. Latz, C. Schillings, Frank H. Grewenig, 2017 .

Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks

Mikael Sunnåker, Jörg Stelling, Christoph Schwab, 2015, PLoS Comput. Biol..

Quantification of Airfoil Geometry-Induced Aerodynamic Uncertainties - Comparison of Approaches

Dishi Liu, Alexander Litvinenko, Volker Schulz, 2015, SIAM/ASA J. Uncertain. Quantification.

Subsampling in ensemble Kalman inversion

J. Latz, C. Schillings, M. Hanu, 2023, ArXiv.

Book of Abstracts

O. Scherzer, A. Teckentrup, C. Schillings, 2011 .

Ensemble-based gradient inference for particle methods in optimization and sampling

C. Totzeck, Philipp Wacker, C. Schillings, 2022, SIAM/ASA Journal on Uncertainty Quantification.

On the convergence of the Laplace approximation and noise-level-robustness of Laplace-based Monte Carlo methods for Bayesian inverse problems

Bjorn Sprungk, Claudia Schillings, Philipp Wacker, 2019, Numerische Mathematik.

On the influence of robustness measures on shape optimization with stochastic uncertainties

Claudia Schillings, Volker Schulz, V. Schulz, 2015 .

Continuous time limit of the stochastic ensemble Kalman inversion: Strong convergence analysis pdf

Dirk Blömker, Claudia Schillings, Philipp Wacker, 2021, SIAM J. Numer. Anal..

On the Nature and Treatment of Uncertainties in Aerodynamic Design

Claudia Schillings, Volker Schulz, V. Schulz, 2008 .

Optimal Aerodynamic Design under Uncertainty

Volker Schulz, Claudia Schillings, V. Schulz, 2013 .

Efficient shape optimization for certain and uncertain aerodynamic design

Claudia Schillings, Volker Schulz, Stephan Schmidt, 2011 .

Convergence analysis of ensemble Kalman inversion: the linear, noisy case

Andrew Stuart, Claudia Schillings, A. Stuart, 2017, 1702.07894.

One-shot Learning of Surrogates in PDE-constrained Optimization Under Uncertainty

Philipp A. Guth, Simon Weissmann, C. Schillings, 2021, 2112.11126.

Ensemble Kalman filter for neural network based one-shot inversion pdf

Claudia Schillings, Simon Weissmann, Philipp A. Guth, 2020, ArXiv.

A General Framework for Machine Learning based Optimization Under Uncertainty pdf

Claudia Schillings, Simon Weissmann, Philipp A. Guth, 2021, ArXiv.

Parabolic PDE-constrained optimal control under uncertainty with entropic risk measure using quasi-Monte Carlo integration

Philipp A. Guth, I. Sloan, Claudia Schillings, 2022, Numerische Mathematik.

A Quasi-Monte Carlo Method for Optimal Control Under Uncertainty

Frances Y. Kuo, Ian H. Sloan, Vesa Kaarnioja, 2021, SIAM/ASA J. Uncertain. Quantification.

Large deformation shape uncertainty quantification in acoustic scattering

Ralf Hiptmair, Christoph Schwab, Claudia Schillings, 2018, Adv. Comput. Math..

Sparse Approximation Algorithms for High Dimensional Parametric Initial Value Problems

Christoph Schwab, Claudia Schillings, Markus Hansen, 2012, HPSC.

Sampling sup‐normalized spectral functions for Brown–Resnick processes

M. Schlather, M. Oesting, C. Schillings, 2019, Stat.

Happy People - Image Synthesis as Black-Box Optimization Problem in the Discrete Latent Space of Deep Generative Models

C. Schillings, M. Keuper, Steffen Jung, 2023, arXiv.org.

Sparse, adaptive Smolyak quadratures for Bayesian inverse problems

Christoph Schwab, Claudia Schillings, C. Schwab, 2013 .

Sparsity in Bayesian inversion of parametric operator equations

C. Schwab, Claudia Schillings, C. Schillings, 2014 .

On the treatment of distributed uncertainties in PDE‐constrained optimization

Alfio Borzì, Volker Schulz, Claudia Schillings, 2010 .

Hierarchical surrogate-based Approximate Bayesian Computation for an electric motor test bench

Vincent Heuveline, Michael Schick, Claudia Schillings, 2021, 2106.09597.

A quasi-Monte Carlo Method for an Optimal Control Problem Under Uncertainty pdf

Frances Y. Kuo, Ian H. Sloan, Vesa Kaarnioja, 2019, ArXiv.

A Strongly Convergent Numerical Scheme from Ensemble Kalman Inversion

Dirk Blömker, Claudia Schillings, Philipp Wacker, 2017, SIAM J. Numer. Anal..

Adaptive Tikhonov strategies for stochastic ensemble Kalman inversion pdf

Neil K. Chada, Xin T. Tong, Claudia Schillings, 2021, Inverse Problems.

On the concentration of subgaussian vectors and positive quadratic forms in Hilbert spaces

Mattes Mollenhauer, C. Schillings, 2023, 2306.11404.

Consistency analysis of bilevel data-driven learning in inverse problems pdf

Neil K. Chada, Xin T. Tong, Claudia Schillings, 2020, Communications in Mathematical Sciences.

On the Incorporation of Box-Constraints for Ensemble Kalman Inversion pdf

Claudia Schillings, Neil K. Chada, Simon Weissmann, 2019, Foundations of Data Science.

Sparse, adaptive Smolyak quadratures for Bayesian inverse problems

C. Schwab, C. Schillings, 2013 .

Sparse, adaptive Smolyak algorithms for Bayesian inverse problems

Christoph Schwab, Claudia Schillings, C. Schwab, 2012 .