| Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model J Pathak, A Wikner, R Fussell, S Chandra, BR Hunt, M Girvan, E Ott Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (4), 2018 | 299 | 2018 |
| A machine learning‐based global atmospheric forecast model T Arcomano, I Szunyogh, J Pathak, A Wikner, BR Hunt, E Ott Geophysical Research Letters 47 (9), e2020GL087776, 2020 | 122 | 2020 |
| Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systems A Wikner, J Pathak, B Hunt, M Girvan, T Arcomano, I Szunyogh, ... Chaos: An Interdisciplinary Journal of Nonlinear Science 30 (5), 2020 | 73 | 2020 |
| Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based components A Wikner, J Pathak, BR Hunt, I Szunyogh, M Girvan, E Ott Chaos: An Interdisciplinary Journal of Nonlinear Science 31 (5), 2021 | 39 | 2021 |
| A hybrid approach to atmospheric modeling that combines machine learning with a physics‐based numerical model T Arcomano, I Szunyogh, A Wikner, J Pathak, BR Hunt, E Ott Journal of Advances in Modeling Earth Systems 14 (3), e2021MS002712, 2022 | 38 | 2022 |
| A hybrid atmospheric model incorporating machine learning can capture dynamical processes not captured by its physics‐based component T Arcomano, I Szunyogh, A Wikner, BR Hunt, E Ott Geophysical Research Letters 50 (8), e2022GL102649, 2023 | 10 | 2023 |
| Data-informed reservoir computing for efficient time-series prediction F Köster, D Patel, A Wikner, L Jaurigue, K Lüdge Chaos: An Interdisciplinary Journal of Nonlinear Science 33 (7), 2023 | 7 | 2023 |
| Chaos Interdiscip J Pathak, A Wikner, R Fussell, S Chandra, BR Hunt, M Girvan, E Ott J. Nonlinear Sci 28 (4), 041101, 2018 | 6 | 2018 |
| Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing A Wikner, J Harvey, M Girvan, BR Hunt, A Pomerance, T Antonsen, E Ott Neural Networks 170, 94-110, 2024 | 3 | 2024 |
| A hybrid atmospheric model incorporating machine learning can capture dynamical processes not captured by its physics-based component T Arcomano, I Szunyogh, A Wikner, BR Hunt, E Ott Authorea Preprints, 2022 | 3 | 2022 |
| Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization A Wikner, J Harvey, M Girvan, BR Hunt, A Pomerance, T Antonsen, E Ott arXiv preprint arXiv:2211.05262, 2022 | 1 | 2022 |
| Hybrid Weather Prediction: A Blend of Machine Learning and Numerical Modeling T Arcomano, I Szunyogh, E Ott, B Hunt, A Wikner AGU Fall Meeting Abstracts 2020, A056-01, 2020 | 1 | 2020 |
| Applications of a Foundation Model Approach for Weather and Climate T Arcomano, A Wikner, R Maulik, VR Kotamarthi, S Foreman AGU23, 2023 | | 2023 |
| Attention-Based Ensemble Pooling for Time Series Forecasting D Patel, A Wikner arXiv preprint arXiv:2310.16231, 2023 | | 2023 |
| Combining Physics-based Modeling, Machine Learning, and Data Assimilation for Forecasting Large, Complex, Spatiotemporally Chaotic Systems AP Wikner | | 2023 |
| Evaluation of a Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a General Circulation Model T Arcomano, I Szunyogh, A Wikner, J Pathak, B Hunt, E Ott AGU Fall Meeting Abstracts 2021, A15Q-08, 2021 | | 2021 |
| DSIPHER: Drug SIde effect Prediction HEuRistic A Pathni, A Wikner, G Somepalli, K Gupta | | |
