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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…
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…