Reducing storage requirements for biological sequence comparison. - Michael Roberts; Wayne B. Hayes; Brian R. Hunt; Stephen M. Mount; James A. Yorke (Bioinform., 2004)

Two Models for the Study of Congested Internet Connections - Ian Frommer; Eric J. Harder; Brian R. Hunt; Ryan Lance; Edward Ott; James A. Yorke (CoRR, 2004)

A Preprocessor for Shotgun Assembly of Large Genomes. - Michael Roberts; Brian R. Hunt; James A. Yorke; Randall A. Bolanos; Arthur L. Delcher (J. Comput. Biol., 2004)

Bifurcations and Chaos in a Periodically probed Computer Network. - Ian Frommer; Eric J. Harder; Brian R. Hunt; Ryan Lance; Edward Ott; James A. Yorke (Int. J. Bifurc. Chaos, 2009)

Stability of TCP Dynamics in Large Data Networks. - Russell D. Halper; Eric J. Harder; Brian R. Hunt; James A. Yorke (SIAM J. Appl. Dyn. Syst., 2009)

Absolute Continuity of a Function and Uniform Integrability of Its Divided Differences. - Patrick M. Fitzpatrick; Brian R. Hunt (Am. Math. Mon., 2015)

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)

Forecasting of Spatio-temporal Chaotic Dynamics with Recurrent Neural Networks: a comparative study of Reservoir Computing and Backpropagation Algorithms. - Pantelis-Rafail Vlachas; Jaideep Pathak; Brian R. Hunt; Themistoklis P. Sapsis; Michelle Girvan; Edward Ott; Petros Koumoutsakos (CoRR, 2019)

Separation of Chaotic Signals by Reservoir Computing. - Sanjukta Krishnagopal; Michelle Girvan; Edward Ott; Brian R. Hunt (CoRR, 2019)

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)

Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics. - Pantelis-Rafail Vlachas; Jaideep Pathak; Brian R. Hunt; Themistoklis P. Sapsis; Michelle Girvan; Edward Ott; Petros Koumoutsakos (Neural Networks, 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)

Exploring the Potential of Hybrid Machine-Learning/Physics-Based Modeling for Atmospheric/Oceanic Prediction Beyond the Medium Range. - Dhruvit Patel; Troy Arcomano; Brian R. Hunt; Istvan Szunyogh; Edward Ott (CoRR, 2024)

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)

Tailored Forecasting from Short Time Series via Meta-learning. - Declan A. Norton; Edward Ott; Andrew Pomerance; Brian R. Hunt; Michelle Girvan (CoRR, 2025)