Sparse Approximation Algorithms for High Dimensional Parametric Initial Value Problems. - Markus Hansen; Claudia Schillings; Christoph Schwab (HPSC, 2012)

Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks. - Claudia Schillings; Mikael Sunnåker; Jörg Stelling; Christoph Schwab (PLoS Comput. Biol., 2015)

Quantification of Airfoil Geometry-Induced Aerodynamic Uncertainties - Comparison of Approaches. - Dishi Liu; Alexander Litvinenko; Claudia Schillings; Volker Schulz 001 (SIAM/ASA J. Uncertain. Quantification, 2017)

Analysis of the Ensemble Kalman Filter for Inverse Problems. - Claudia Schillings; Andrew M. Stuart (SIAM J. Numer. Anal., 2017)

Large deformation shape uncertainty quantification in acoustic scattering. - Ralf Hiptmair; Laura Scarabosio; Claudia Schillings; Christoph Schwab (Adv. Comput. Math., 2018)

A Strongly Convergent Numerical Scheme from Ensemble Kalman Inversion. - Dirk Blömker; Claudia Schillings; Philipp Wacker (SIAM J. Numer. Anal., 2018)

On the Incorporation of Box-Constraints for Ensemble Kalman Inversion. - Neil K. Chada; Claudia Schillings; Simon Weissmann (CoRR, 2019)

A quasi-Monte Carlo Method for an Optimal Control Problem Under Uncertainty. - Philipp A. Guth; Vesa Kaarnioja; Frances Y. Kuo; Claudia Schillings; Ian H. Sloan (CoRR, 2019)

Ensemble Kalman filter for neural network based one-shot inversion. - Philipp A. Guth; Claudia Schillings; Simon Weissmann (CoRR, 2020)

Consistency analysis of bilevel data-driven learning in inverse problems. - Neil K. Chada; Claudia Schillings; Xin T. Tong; Simon Weissmann (CoRR, 2020)

On the convergence of the Laplace approximation and noise-level-robustness of Laplace-based Monte Carlo methods for Bayesian inverse problems. - Claudia Schillings; Björn Sprungk; Philipp Wacker (Numerische Mathematik, 2020)

Adaptive Tikhonov strategies for stochastic ensemble Kalman inversion. - Simon Weissmann; Neil K. Chada; Claudia Schillings; Xin T. Tong (CoRR, 2021)

A General Framework for Machine Learning based Optimization Under Uncertainty. - Philipp A. Guth; Claudia Schillings; Simon Weissmann (CoRR, 2021)

A Quasi-Monte Carlo Method for Optimal Control Under Uncertainty. - Philipp A. Guth; Vesa Kaarnioja; Frances Y. Kuo; Claudia Schillings; Ian H. Sloan (SIAM/ASA J. Uncertain. Quantification, 2021)

Continuous Time Limit of the Stochastic Ensemble Kalman Inversion: Strong Convergence Analysis. - Dirk Blömker; Claudia Schillings; Philipp Wacker; Simon Weissmann (SIAM J. Numer. Anal., 2022)

Subsampling in ensemble Kalman inversion. - Matei Hanu; Jonas Latz; Claudia Schillings (CoRR, 2023)

Happy People - Image Synthesis as Black-Box Optimization Problem in the Discrete Latent Space of Deep Generative Models. - Steffen Jung 001; Jan Christian Schwedhelm; Claudia Schillings; Margret Keuper (CoRR, 2023)

Ensemble Kalman Inversion for Image Guided Guide Wire Navigation in Vascular Systems. - Matei Hanu; Jürgen Hesser; Guido Kanschat; Javier Moviglia; Claudia Schillings; Jan Stallkamp (CoRR, 2023)

Ensemble-Based Gradient Inference for Particle Methods in Optimization and Sampling. - Claudia Schillings; Claudia Totzeck; Philipp Wacker (SIAM/ASA J. Uncertain. Quantification, 2023)

Generative Modelling with Tensor Train approximations of Hamilton-Jacobi-Bellman equations. - David Sommer; Robert Gruhlke; Max Kirstein; Martin Eigel; Claudia Schillings (CoRR, 2024)

Quasi-Monte Carlo for Bayesian design of experiment problems governed by parametric PDEs. - Vesa Kaarnioja; Claudia Schillings (CoRR, 2024)

One-Shot Learning of Surrogates in PDE-Constrained Optimization under Uncertainty. - Philipp A. Guth; Claudia Schillings; Simon Weissmann (SIAM/ASA J. Uncertain. Quantification, 2024)

Parabolic PDE-constrained optimal control under uncertainty with entropic risk measure using quasi-Monte Carlo integration. - Philipp A. Guth; Vesa Kaarnioja; Frances Y. Kuo; Claudia Schillings; Ian H. Sloan (Numerische Mathematik, 2024)

Lattice Rules Meet Kernel Cubature. - Vesa Kaarnioja; Ilja Klebanov; Claudia Schillings; Yuya Suzuki (CoRR, 2025)

Uncertainty quantification for stationary and time-dependent PDEs subject to Gevrey regular random domain deformations. - Ana Djurdjevac; Vesa Kaarnioja; Claudia Schillings; André-Alexander Zepernick (CoRR, 2025)

Quasi-Monte Carlo for Bayesian shape inversion governed by the Poisson problem subject to Gevrey regular domain deformations. - Ana Djurdjevac; Vesa Kaarnioja; Max Orteu; Claudia Schillings (CoRR, 2025)

Accuracy Boost in Ensemble Kalman Inversion via Ensemble Control Strategies. - Ruben Harris; Claudia Schillings (CoRR, 2025)

Gradient-Free Sequential Bayesian Experimental Design via Interacting Particle Systems. - Robert Gruhlke; Matei Hanu; Claudia Schillings; Philipp Wacker (CoRR, 2025)