Structure Preserving Model Reduction of Parametric Hamiltonian Systems. - Babak Maboudi Afkham; Jan S. Hesthaven (SIAM J. Sci. Comput., 2017)

Structure-Preserving Model-Reduction of Dissipative Hamiltonian Systems. - Babak Maboudi Afkham; Jan S. Hesthaven (J. Sci. Comput., 2019)

Learning Regularization Parameters of Inverse Problems via Deep Neural Networks. - Babak Maboudi Afkham; Julianne Chung; Matthias Chung (CoRR, 2021)

Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks. - Babak Maboudi Afkham; Julianne Chung; Matthias Chung (CoRR, 2023)

Inferring Features with Uncertain Roughness. - Babak Maboudi Afkham; Nicolai André Brogaard Riis; Yiqiu Dong; Per Christian Hansen (CoRR, 2023)

CUQIpy - Part I: computational uncertainty quantification for inverse problems in Python. - Nicolai André Brogaard Riis; Amal Mohammed A. Alghamdi; Felipe Uribe; Silja L. Christensen; Babak Maboudi Afkham; Per Christian Hansen; Jakob Sauer Jørgensen (CoRR, 2023)

CUQIpy - Part II: computational uncertainty quantification for PDE-based inverse problems in Python. - Amal Mohammed A. Alghamdi; Nicolai André Brogaard Riis; Babak Maboudi Afkham; Felipe Uribe; Silja L. Christensen; Per Christian Hansen; Jakob Sauer Jørgensen (CoRR, 2023)

Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography. - Babak Maboudi Afkham; Yiqiu Dong; Per Christian Hansen (SIAM/ASA J. Uncertain. Quantification, 2023)

Inferring Object Boundaries and Their Roughness with Uncertainty Quantification. - Babak Maboudi Afkham; Nicolai André Brogaard Riis; Yiqiu Dong; Per Christian Hansen (J. Math. Imaging Vis., 2024)

Bayesian decomposition using Besov priors. - Andreas Horst; Babak Maboudi Afkham; Yiqiu Dong; Jakob Lemvig (CoRR, 2025)

Uncertainty Quantification for Linear Inverse Problems with Besov Prior: A Randomize-Then-Optimize Method. - Andreas Horst; Babak Maboudi Afkham; Yiqiu Dong; Jakob Lemvig (Stat. Comput., 2025)