Low-rank solvers for unsteady Stokes–Brinkman optimal control problem with random data P Benner, S Dolgov, A Onwunta, M Stoll Computer Methods in Applied Mechanics and Engineering 304, 26-54, 2016 | 65 | 2016 |

Block-diagonal preconditioning for optimal control problems constrained by PDEs with uncertain inputs P Benner, A Onwunta, M Stoll SIAM Journal on Matrix Analysis and Applications 37 (2), 491-518, 2016 | 51 | 2016 |

Low-rank solution of unsteady diffusion equations with stochastic coefficients P Benner, A Onwunta, M Stoll SIAM/ASA Journal on Uncertainty Quantification 3 (1), 622-649, 2015 | 44 | 2015 |

Novel deep neural networks for solving bayesian statistical inverse H Antil, HC Elman, A Onwunta, D Verma arXiv preprint arXiv:2102.03974, 2021 | 21 | 2021 |

Validating structural credit portfolio models M Kalkbrener, A Onwunta Model risk—identification, measurement and management. Risk Books, London …, 2010 | 21 | 2010 |

Low‐rank solution of an optimal control problem constrained by random Navier‐Stokes equations P Benner, S Dolgov, A Onwunta, M Stoll International Journal for Numerical Methods in Fluids 92 (11), 1653-1678, 2020 | 14 | 2020 |

A low-rank inexact Newton–Krylov method for stochastic eigenvalue problems P Benner, A Onwunta, M Stoll Computational Methods in Applied Mathematics 19 (1), 5-22, 2019 | 13 | 2019 |

Solving optimal control problems governed by random Navier-Stokes equations using low-rank methods P Benner, S Dolgov, A Onwunta, M Stoll arXiv preprint arXiv:1703.06097, 2017 | 11 | 2017 |

Optimal control, numerics, and applications of fractional PDEs H Antil, T Brown, R Khatri, A Onwunta, D Verma, M Warma Handbook of Numerical Analysis 23, 87-114, 2022 | 9 | 2022 |

TTRISK: Tensor train decomposition algorithm for risk averse optimization H Antil, S Dolgov, A Onwunta Numerical Linear Algebra with Applications 30 (3), e2481, 2023 | 8 | 2023 |

A deep neural network approach for parameterized PDEs and Bayesian inverse problems H Antil, HC Elman, A Onwunta, D Verma Machine Learning: Science and Technology 4 (3), 035015, 2023 | 7 | 2023 |

On the existence and uniqueness of the solution of a parabolic optimal control problem with uncertain inputs P Benner, A Onwunta, M Stoll arXiv preprint arXiv:1809.10645, 2018 | 5 | 2018 |

Threshold accepting for credit risk assessment and validation M Lyra, A Onwunta, P Winker Journal of Banking Regulation 16, 130-145, 2015 | 5 | 2015 |

State-constrained optimization problems under uncertainty: A tensor train approach H Antil, S Dolgov, A Onwunta arXiv preprint arXiv:2301.08684, 2023 | 3 | 2023 |

Deep nonnegative matrix factorization with beta divergences V Leplat, LTK Hien, A Onwunta, N Gillis Neural Computation 36 (11), 2365-2402, 2024 | 2 | 2024 |

Reduced-order modeling for nonlinear Bayesian statistical inverse problems HC Elman, A Onwunta arXiv preprint arXiv:1909.02539, 2019 | 2 | 2019 |

Efficient Score Matching with Deep Equilibrium Layers Y Huang, Q Wang, A Onwunta, B Wang The Twelfth International Conference on Learning Representations, 2024 | 1 | 2024 |

Low-Rank Iterative Solvers for Large-Scale Stochastic Galerkin Linear Systems A Onwunta Otto-von-Guericke Universität Magdeburg, 2016 | 1 | 2016 |

COMISEF WORKING PAPERS SERIES M Lyra, A Onwunta, P Winker | 1 | 2010 |

On the regularity of refinable functions AA Onwunta Stellenbosch: University of Stellenbosch, 2006 | 1 | 2006 |