57 citations · 94 across the 16 of their papers we have counts for
11 papers · 1 filter
Ensemble Kalman inversion with non-smooth regularization
Simon Weissmann
This paper investigates ensemble Kalman inversion (EKI) for variational inverse problems with convex, potentially non-smooth regularization. While deterministic EKI and its Tikhono…
The Ensemble Kalman Filter for Dynamic Inverse Problems
Simon Weissmann, Neil K. Chada, Xin T. Tong
In inverse problems, the goal is to estimate unknown model parameters from noisy observational data. Traditionally, inverse problems are solved under the assumption of a fixed forw…
On the ensemble Kalman inversion under inequality constraints
Matei Hanu, Simon Weissmann
The ensemble Kalman inversion (EKI), a recently introduced optimisation method for solving inverse problems, is widely employed for the efficient and derivative-free estimation of…
Adaptive multilevel subset simulation with selective refinement
Daniel Elfverson, Robert Scheichl, Simon Weissmann +1
In this work we propose an adaptive multilevel version of subset simulation to estimate the probability of rare events for complex physical systems. Given a sequence of nested fail…
Gradient flow structure and convergence analysis of the ensemble Kalman inversion for nonlinear forward models
Simon Weissmann
The ensemble Kalman inversion (EKI) is a particle based method which has been introduced as the application of the ensemble Kalman filter to inverse problems. In practice it has be…
Adaptive Tikhonov strategies for stochastic ensemble Kalman inversion
Simon Weissmann, Neil K. Chada, Claudia Schillings +1
Ensemble Kalman inversion (EKI) is a derivative-free optimizer aimed at solving inverse problems, taking motivation from the celebrated ensemble Kalman filter. The purpose of this…
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…
Well Posedness and Convergence Analysis of the Ensemble Kalman Inversion
Dirk Blömker, Claudia Schillings, Philipp Wacker +1
The ensemble Kalman inversion is widely used in practice to estimate unknown parameters from noisy measurement data. Its low computational costs, straightforward implementation, an…