7 citations · 10 across the 5 of their papers we have counts for
9 papers
A Gauss-Newton Method for ODE Optimal Tracking Control
Vicky Holfeld, Michael Burger, Claudia Schillings
This paper introduces and analyses a continuous optimization approach to solve optimal control problems involving ordinary differential equations (ODEs) and tracking type objective…
Continuous time limit of the stochastic ensemble Kalman inversion: Strong convergence analysis
Dirk Blömker, Claudia Schillings, Philipp Wacker +1
The Ensemble Kalman inversion (EKI) method is a method for the estimation of unknown parameters in the context of (Bayesian) inverse problems. The method approximates the underlyin…
Hierarchical surrogate-based Approximate Bayesian Computation for an electric motor test bench
David N. John, Livia Stohrer, Claudia Schillings +2
Inferring parameter distributions of complex industrial systems from noisy time series data requires methods to deal with the uncertainty of the underlying data and the used simula…
Consistency analysis of bilevel data-driven learning in inverse problems
Neil K. Chada, Claudia Schillings, Xin T. Tong +1
One fundamental problem when solving inverse problems is how to find regularization parameters. This article considers solving this problem using data-driven bilevel optimization,…
Ensemble Kalman filter for neural network based one-shot inversion
Philipp A. Guth, Claudia Schillings, Simon Weissmann
We study the use of novel techniques arising in machine learning for inverse problems. Our approach replaces the complex forward model by a neural network, which is trained simulta…
A quasi-Monte Carlo Method for an Optimal Control Problem Under Uncertainty
Philipp A. Guth, Vesa Kaarnioja, Frances Y. Kuo +2
We study an optimal control problem under uncertainty, where the target function is the solution of an elliptic partial differential equation with random coefficients, steered by a…