Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model. - Jaideep Pathak; Alexander Wikner; Rebeckah Fussell; Sarthak Chandra; Brian R. Hunt; Michelle Girvan; Edward Ott (CoRR, 2018)

Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems. - Alexander Wikner; Jaideep Pathak; Brian R. Hunt; Michelle Girvan; Troy Arcomano; Istvan Szunyogh; Andrew Pomerance; Edward Ott (CoRR, 2020)

Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components. - Alexander Wikner; Jaideep Pathak; Brian R. Hunt; Istvan Szunyogh; Michelle Girvan; Edward Ott (CoRR, 2021)

Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization. - Alexander Wikner; Brian R. Hunt; Joseph Harvey; Michelle Girvan; Edward Ott (CoRR, 2022)

Attention-Based Ensemble Pooling for Time Series Forecasting. - Dhruvit Patel; Alexander Wikner (CoRR, 2023)

Combining Physics-based Modeling, Machine Learning, and Data Assimilation for Forecasting Large, Complex, Spatiotemporally Chaotic Systems. - Alexander Wikner (2023)

Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing. - Alexander Wikner; Joseph Harvey; Michelle Girvan; Brian R. Hunt; Andrew Pomerance; Thomas M. Antonsen Jr.; Edward Ott (Neural Networks, 2024)