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A Stochastic Iteratively Regularized Gauss-Newton Method
El Houcine Bergou, Neil K. Chada, Youssef Diouane
This work focuses on developing and motivating a stochastic version of a wellknown inverse problem methodology. Specifically, we consider the iteratively regularized Gauss-Newton m…
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…
The Stochastic Steepest Descent Method for Robust Optimization in Banach Spaces
Neil K. Chada, Philip J. Herbert
Stochastic gradient methods have been a popular and powerful choice of optimization methods, aimed at minimizing functions. Their advantage lies in the fact that that one approxima…
Multilevel Ensemble Kalman-Bucy Filters
Neil K. Chada, Ajay Jasra, Fangyuan Yu
In this article we consider the linear filtering problem in continuous-time. We develop and apply multilevel Monte Carlo (MLMC) strategies for ensemble Kalman-Bucy filters (EnKBFs)…
Iterative Ensemble Kalman Methods: A Unified Perspective with Some New Variants
Neil K. Chada, Yuming Chen, Daniel Sanz-Alonso
This paper provides a unified perspective of iterative ensemble Kalman methods, a family of derivative-free algorithms for parameter reconstruction and other related tasks. We iden…
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…