447 citations · 806 across the 17 of their papers we have counts for
19 papers
A variational framework for Bayesian inversion in moving boundary Darcy flow
Michael E. Causon, Marco A. Iglesias, Michael V. Tretyakov
We develop an infinite-dimensional Bayesian framework for recovering spatially varying log-permeability from pressure observations in single-phase Darcy flow with a moving boundary…
DeepONet-accelerated Bayesian inversion for moving boundary problems
Marco A. Iglesias, Michael. E. Causon, Mikhail Y. Matveev +2
This work demonstrates that neural operator learning provides a powerful and flexible framework for building fast, accurate emulators of moving boundary systems, enabling their int…
On a Dynamic Variant of the Iteratively Regularized Gauss-Newton Method with Sequential Data
Neil K. Chada, Marco A. Iglesias, Shuai Lu +1
For numerous parameter and state estimation problems, assimilating new data as they become available can help produce accurate and fast inference of unknown quantities. While most…
Adaptive regularisation for ensemble Kalman inversion
Marco Iglesias, Yuchen Yang
We propose a new regularisation strategy for the classical ensemble Kalman inversion (EKI) framework. The strategy consists of: (i) an adaptive choice for the regularisation parame…
Transform-based particle filtering for elliptic Bayesian inverse problems
Sangeetika Ruchi, Svetlana Dubinkina, Marco Iglesias
We introduce optimal transport based resampling in adaptive SMC. We consider elliptic inverse problems of inferring hydraulic conductivity from pressure measurements. We consider t…
Ensemble-marginalized Kalman filter for linear time-dependent PDEs with noisy boundary conditions: Application to heat transfer in building walls
Marco Iglesias, Zaid Sawlan, Marco Scavino +2
In this work, we present the ensemble-marginalized Kalman filter (EnMKF), a sequential algorithm analogous to our previously proposed approach [1,2], for estimating the state and p…