Publications

Arcomano, T., Szunyogh, I., Wikner, A., Hunt, B. R., & Ott, E. (2022). A Hybrid Atmospheric Model Incorporating Machine Learning Can Capture Dynamical Processes Not Captured by Its Physics-Based Component ESS Open Archive (Preprint).

Wikner, A., Harvey, J., Girvan, M., Hunt, B. R., Pomerance, A., Antonsen, T., & Ott, E. (2022). Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization. axXiv (Preprint).

Patel, D., & Ott, E. (2022). Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systems. arXiv (Preprint).

Arcomano, T., Szunyogh, I., Wikner, A., Pathak, J., Hunt, B. R., & Ott, E. (2022). A hybrid approach to atmospheric modeling that combines machine learning with a physics-based numerical model. Journal of Advances in Modeling Earth Systems, 14, e2021MS002712.

Wikner, A., Pathak, J., Hunt, B., Szunyogh, I., Girvan, M., & Ott, E. (2021). Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based components. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31(5), 053114.

Patel, D., Canaday, D., Girvan, M., Pomerance, A., & Ott, E. (2021). Using machine learning to predict statistical properties of non-stationary dynamical processes: System climate,regime transitions, and the effect of stochasticity. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31(3), 033149.

Arcomano, T., Szunyogh, I., Pathak, J., Wikner, A., Hunt, B., & Ott, E. (2020). A Machine Learning-Based Global Atmospheric Forecast Model. Geophysical Research Letters, 47(9), e2020GL087776.

Wikner, A., Pathak, J., Hunt, B., Girvan, M., Arcomano, T., Szunyogh, I., Pomerance, A., & Ott, E. (2020). Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(5), 053111.