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Dunbar, Oliver
- Dunbar, Oliver R. A. and Elliott, Charles M., el al. (2025) Models for information propagation on graphs; European Journal of Applied Mathematics; Vol. 36; No. 5; 1040-1061; 10.1017/s0956792524000895
- Vernon, Sydney and Bach, Eviatar, el al. (2025) Nesterov acceleration for ensemble Kalman inversion and variants; Journal of Computational Physics; Vol. 535; 114063; 10.1016/j.jcp.2025.114063
- Dunbar, Oliver R. A. and Nelsen, Nicholas H., el al. (2025) Hyperparameter optimization for randomized algorithms: a case study on random features; Statistics and Computing; Vol. 35; No. 3; 56; 10.1007/s11222-025-10587-w
- Christopoulos, Costa and Lopez‐Gomez, Ignacio, el al. (2024) Online Learning of Entrainment Closures in a Hybrid Machine Learning Parameterization; Journal of Advances in Modeling Earth System (JAMES); Vol. 16; No. 11; e2024MS004485; 10.1029/2024ms004485
- Dunbar, Oliver R. A. and Howland, Michael F., el al. (2022) Ensemble-Based Experimental Design for Targeting Data Acquisition to Inform Climate Models; Journal of Advances in Modeling Earth Systems; Vol. 14; No. 9; Art. No. e2022MS002997; 10.1029/2022ms002997
- Lopez-Gomez, Ignacio and Christopoulos, Costa, el al. (2022) Training physics‐based machine‐learning parameterizations with gradient‐free ensemble Kalman methods; Journal of Advances in Modeling Earth Systems; Art. No. e2022MS003105; 10.1029/2022ms003105
- Bieli, Melanie and Dunbar, Oliver R. A., el al. (2022) An Efficient Bayesian Approach to Learning Droplet Collision Kernels: Proof of Concept Using "Cloudy," a New n-Moment Bulk Microphysics Scheme; Journal of Advances in Modeling Earth Systems; Vol. 14; No. 8; Art. No. e2022MS002994; 10.1029/2022ms002994
- Schneider, Tapio and Dunbar, Oliver R. A., el al. (2022) Epidemic management and control through risk-dependent individual contact interventions; PLoS Computational Biology; Vol. 18; No. 6; Art. No. e1010171; PMCID PMC9223336; 10.1371/journal.pcbi.1010171
- Dunbar, Oliver R. A. and Duncan, Andrew B., el al. (2022) Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods; SIAM Journal on Applied Dynamical Systems; Vol. 21; No. 2; 1539-1572; 10.1137/21M1410853
- Lopez-Gomez, Ignacio and Christopoulos, Costa, el al. (2022) Training physics-based machine-learning parameterizations with gradient-free ensemble Kalman methods; 10.1002/essoar.10510937.2
- Howland, Michael F. and Dunbar, Oliver R. A., el al. (2022) Parameter Uncertainty Quantification in an Idealized GCM With a Seasonal Cycle; Journal of Advances in Modelling Earth Systems; Vol. 14; No. 3; Art. No. e2021MS002735; 10.1029/2021MS002735
- Bieli, Melanie and Dunbar, Oliver R. A., el al. (2022) An efficient Bayesian approach to learning droplet collision kernels: Proof of concept using "Cloudy", a new n-moment bulk microphysics scheme; 10.1002/essoar.10510248.1
- Dunbar, Oliver R. A. and Howland, Michael F., el al. (2022) Ensemble-Based Experimental Design for Targeted High-Resolution Simulations to Inform Climate Models; 10.1002/essoar.10510142.1
- Dunbar, Oliver R. A. and Garbuno-Inigo, Alfredo, el al. (2021) Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM; Journal of Advances in Modelling Earth Systems; Vol. 13; No. 9; Art. No. e2020MS002454; 10.1029/2020MS002454
