59 citations · 123 across the 11 of their papers we have counts for
12 papers
Robust A-Optimal Experimental Design for Bayesian Inverse Problems
Ahmed Attia, Sven Leyffer, Todd Munson
Optimal design of experiments for Bayesian inverse problems has recently gained wide popularity and attracted much attention, especially in the computational science and Bayesian i…
PyOED: An Extensible Suite for Data Assimilation and Model-Constrained Optimal Design of Experiments
Abhijit Chowdhary, Shady E. Ahmed, Ahmed Attia
This paper describes PyOED, a highly extensible scientific package that enables developing and testing model-constrained optimal experimental design (OED) for inverse problems. Spe…
Stochastic Learning Approach to Binary Optimization for Optimal Design of Experiments
Ahmed Attia, Sven Leyffer, Todd Munson
We present a novel stochastic approach to binary optimization for optimal experimental design (OED) for Bayesian inverse problems governed by mathematical models such as partial di…
Optimal Experimental Design for Inverse Problems in the Presence of Observation Correlations
Ahmed Attia, Emil Constantinescu
Optimal experimental design (OED) is the general formalism of sensor placement and decisions about the data collection strategy for engineered or natural experiments. This approach…
An Optimal Experimental Design Framework for Adaptive Inflation and Covariance Localization for Ensemble Filters
Ahmed Attia, Emil Constantinescu
We develop an optimal experimental design framework for adapting the covariance inflation and localization in data assimilation problems. Covariance inflation and localization are…
A Machine Learning Approach to Adaptive Covariance Localization
Azam Moosavi, Ahmed Attia, Adrian Sandu
Data assimilation plays a key role in large-scale atmospheric weather forecasting, where the state of the physical system is estimated from model outputs and observations, and is t…
Goal-Oriented Optimal Design of Experiments for Large-Scale Bayesian Linear Inverse Problems
Ahmed Attia, Alen Alexanderian, Arvind K. Saibaba
We develop a framework for goal-oriented optimal design of experiments (GOODE) for large-scale Bayesian linear inverse problems governed by PDEs. This framework differs from classi…
DATeS: A Highly-Extensible Data Assimilation Testing Suite v1.0
Ahmed Attia, Adrian Sandu
A flexible and highly-extensible data assimilation testing suite, named DATeS, is described in this paper. DATeS aims to offer a unified testing environment that allows researchers…
Cluster Sampling Filters for Non-Gaussian Data Assimilation
Ahmed Attia, Azam Moosavi, Adrian Sandu
This paper presents a fully non-Gaussian version of the Hamiltonian Monte Carlo (HMC) sampling filter. The Gaussian prior assumption in the original HMC filter is relaxed. Specific…
The Reduced-Order Hybrid Monte Carlo Sampling Smoother
Ahmed Attia, Razvan Stefanescu, Adrian Sandu
Hybrid Monte-Carlo (HMC) sampling smoother is a fully non-Gaussian four-dimensional data assimilation algorithm that works by directly sampling the posterior distribution formulate…
A Hybrid Monte-Carlo Sampling Smoother for Four Dimensional Data Assimilation
Ahmed Attia, Vishwas Rao, Adrian Sandu
This paper constructs an ensemble-based sampling smoother for four-dimensional data assimilation using a Hybrid/Hamiltonian Monte-Carlo approach. The smoother samples efficiently f…
A Sampling Filter for Non-Gaussian Data Assimilation
Ahmed Attia, Adrian Sandu
Data assimilation combines information from models, measurements, and priors to estimate the state of a dynamical system such as the atmosphere. The Ensemble Kalman filter (EnKF) i…