Claudia Schillings

According to our database, Claudia Schillings authored at least 21 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Generative Modelling with Tensor Train approximations of Hamilton-Jacobi-Bellman equations.

David Sommer, Robert Gruhlke, Max Kirstein, Martin Eigel, Claudia Schillings

CoRR, 2024

2023

Ensemble-Based Gradient Inference for Particle Methods in Optimization and Sampling.

Claudia Schillings, Claudia Totzeck, Philipp Wacker

SIAM/ASA J. Uncertain. Quantification, September, 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

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

Steffen Jung, Jan Christian Schwedhelm, Claudia Schillings, Margret Keuper

CoRR, 2023

Subsampling in ensemble Kalman inversion.

Matei Hanu, Jonas Latz, Claudia Schillings

CoRR, 2023

2022

Continuous Time Limit of the Stochastic Ensemble Kalman Inversion: Strong Convergence Analysis.

Dirk Blömker, Claudia Schillings, Philipp Wacker, Simon Weissmann

SIAM J. Numer. Anal., December, 2022

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

CoRR, 2022

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

A General Framework for Machine Learning based Optimization Under Uncertainty.

Philipp A. Guth, Claudia Schillings, Simon Weissmann

CoRR, 2021

Adaptive Tikhonov strategies for stochastic ensemble Kalman inversion.

Simon Weissmann, Neil K. Chada, Claudia Schillings, Xin T. Tong

CoRR, 2021

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

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

Neil K. Chada, Claudia Schillings, Xin T. Tong, Simon Weissmann

CoRR, 2020

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

Philipp A. Guth, Claudia Schillings, Simon Weissmann

CoRR, 2020

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

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

Neil K. Chada, Claudia Schillings, Simon Weissmann

CoRR, 2019

2018

A Strongly Convergent Numerical Scheme from Ensemble Kalman Inversion.

Dirk Blömker, Claudia Schillings, Philipp Wacker

SIAM J. Numer. Anal., 2018

Large deformation shape uncertainty quantification in acoustic scattering.

Ralf Hiptmair, Laura Scarabosio, Claudia Schillings, Christoph Schwab

Adv. Comput. Math., 2018

2017

Analysis of the Ensemble Kalman Filter for Inverse Problems.

Claudia Schillings, Andrew M. Stuart

SIAM J. Numer. Anal., 2017

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

Dishi Liu, Alexander Litvinenko, Claudia Schillings, Volker Schulz

SIAM/ASA J. Uncertain. Quantification, 2017

2015

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

Claudia Schillings, Mikael Sunnåker, Jörg Stelling, Christoph Schwab

PLoS Comput. Biol., 2015

2012

Sparse Approximation Algorithms for High Dimensional Parametric Initial Value Problems.

Markus Hansen, Claudia Schillings, Christoph Schwab

Proceedings of the Modeling, Simulation and Optimization of Complex Processes, 2012