[2020]
- Ahmed Attia and Emil Constantinescu, (2020). Optimal Experimental Design for Inverse Problems in the Presence of Observation Correlations. Submitted.
[2019]
- Ahmed Attia and Emil Constantinescu, (2019). An Optimal Experimental Design Framework for Adaptive Inflation and Covariance Localization for Ensemble Filters. Check the 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. See the Technical Report.
- Azam Moosavi, Ahmed Attia, and Adrian Sandu (2018). "A machine learning approach to adaptive covariance localization. arXiv preprint". Under review. Check the Technical Report.
- Ahmed Attia, Azam Moosavi, and Adrian Sandu (2017). Cluster Sampling Filters for Non-Gaussian Data Assimilation Atmosphere, 9(6), 2018.
- Ahmed Attia and Adrian Sandu (2018). "DATeS: A Highly-Extensible Data Assimilation Testing Suite, Version 1.0". GMD; Under review. Check the 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.
- 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.
[2014 - 2016]
- 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.
- 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.
Citations