2 citations · 2 across the 1 of their papers we have counts for
4 papers
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…
Fokker-Planck particle systems for Bayesian inference: Computational approaches
Sebastian Reich, Simon Weissmann
Bayesian inference can be embedded into an appropriately defined dynamics in the space of probability measures. In this paper, we take Brownian motion and its associated Fokker--Pl…
On the Incorporation of Box-Constraints for Ensemble Kalman Inversion
Neil K. Chada, Claudia Schillings, Simon Weissmann
The Bayesian approach to inverse problems is widely used in practice to infer unknown parameters from noisy observations. In this framework, the ensemble Kalman inversion has been…