18 citations · 18 across the 3 of their papers we have counts for
6 papers
Sampling Methods for Bayesian Inference Involving Convergent Noisy Approximations of Forward Maps
Giacomo Garegnani
We present Bayesian techniques for solving inverse problems which involve mean-square convergent random approximations of the forward map. Noisy approximations of the forward map a…
Robust Estimation of Effective Diffusions from Multiscale Data
Giacomo Garegnani, Andrea Zanoni
We present a novel methodology based on filtered data and moving averages for estimating effective dynamics from observations of multiscale systems. We show in a semi-parametric fr…
A probabilistic finite element method based on random meshes: Error estimators and Bayesian inverse problems
Assyr Abdulle, Giacomo Garegnani
We present a novel probabilistic finite element method (FEM) for the solution and uncertainty quantification of elliptic partial differential equations based on random meshes, whic…
Drift Estimation of Multiscale Diffusions Based on Filtered Data
Assyr Abdulle, Giacomo Garegnani, Grigorios A. Pavliotis +2
We study the problem of drift estimation for two-scale continuous time series. We set ourselves in the framework of overdamped Langevin equations, for which a single-scale surrogat…
Ensemble Kalman filter for multiscale inverse problems
Assyr Abdulle, Giacomo Garegnani, Andrea Zanoni
We present a novel algorithm based on the ensemble Kalman filter to solve inverse problems involving multiscale elliptic partial differential equations. Our method is based on nume…
Random time step probabilistic methods for uncertainty quantification in chaotic and geometric numerical integration
Assyr Abdulle, Giacomo Garegnani
A novel probabilistic numerical method for quantifying the uncertainty induced by the time integration of ordinary differential equations (ODEs) is introduced. Departing from the c…