Ahmed Attia, Sven Leyffer, and Todd Munson, (2022).
Stochastic Learning Approach for Binary Optimization: Application to Bayesian Optimal Design of Experiments.
SIAM Journal on Scientific Computing 44, no. 2 (2022): B395-B427
Published Article.
Technical Report.
[2021]
Ahmed Attia and Emil Constantinescu, (2021).
Optimal Experimental Design for Inverse Problems in the Presence of Observation Correlations.
Submitted.
Technical Report.
[2019]
Ahmed Attia and Emil Constantinescu, (2019). An Optimal Experimental Design Framework for Adaptive Inflation and Covariance Localization for Ensemble Filters. Technical Report.
Azam Moosavi, Ahmed Attia, and Adrian Sandu, (2019). Tuning Covariance Localization using Machine Learning. International Conference on Computational Science (ICCS), pp. 199-212. Springer, Cham, 2019.
[2018]
Ahmed Attia, Alen Alexanderian, and Arvind Karishna Saibaba, (2018).
Goal-Oriented Optimal Design of Experiments for Large-Scale Bayesian Linear Inverse Problems. Inverse Problems.
Published Article.
Technical Report.
Ahmed Attia and Adrian Sandu (2018).
DATeS: A Highly-Extensible Data Assimilation Testing Suite, Version 1.0. GMD.
Published Article.
Ahmed Attia, Azam Moosavi, and Adrian Sandu, (2018).
Cluster Sampling Filters for Non-Gaussian Data Assimilation. Atmosphere, 9(6).
Published Article.
Azam Moosavi, Ahmed Attia, and Adrian Sandu (2018).
A machine learning approach to adaptive covariance localization. arXiv preprint.
Technical Report.
[2017]
Ahmed Attia, Razvan Stefanescu and Adrian Sandu (2017).
The Reduced-Order Hybrid Monte Carlo Sampling Smoother.
International Journal for Numerical Methods in Fluids, doi:10.1002/fld.4255.
Published Article.
Ahmed Attia, Vishwas Rao and Adrian Sandu (2017).
A Hybrid Monte-Carlo Sampling Smoother for Four Dimensional Data Assimilation.
International Journal for Numerical Methods in Fluids, doi: 10.1002/fld.4259.
Published Article.
[2015]
Ahmed Attia, Vishwas Rao and Adrian Sandu (2015).
A Sampling Approach for Four Dimensional Data Assimilation.
Dynamic Data-Driven Environmental Systems Science, MIT. 8964: 215-226.
Published Article.
Ahmed Attia and Adrian Sandu (2015).
A Hybrid Monte Carlo Sampling Filter for non-Gaussian Data Assimilation.
AIMS Geosciences, 1(geosci-01-00041):41-78.
Published Article.