Chaotic Behavior of Deterministic Dissipative Systems (Milos Marek and Igor Schreiber). - Edward Ott (SIAM Rev., 1996)

Combating channel distortions for chaotic signals. - Naresh Sharma; Edward Ott (ICASSP, 1999)

Exploiting Synchronization to Combat Channel distortions in Communication with Chaotic Systems. - Naresh Sharma; Edward Ott (Int. J. Bifurc. Chaos, 2000)

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)

Phase Synchronization in a Modulated Chaotic Laser Array. - David J. Deshazer; Romulus Breban; Edward Ott; Rajarshi Roy 004 (Int. J. Bifurc. Chaos, 2004)

Basin of attraction. - Edward Ott (Scholarpedia, 2006)

Controlling chaos. - Edward Ott (Scholarpedia, 2006)

Crises. - Edward Ott (Scholarpedia, 2006)

Attractor dimensions. - Edward Ott (Scholarpedia, 2008)

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)

Robustness of Network Measures to Link Errors. - John Platig; Edward Ott; Michelle Girvan (CoRR, 2013)

Spatially embedded growing small-world networks. - Ari Zitin; Alex Gorowora; Shane Squires; Mark Herrera; Thomas M. Antonsen Jr.; Michelle Girvan; Edward Ott (CoRR, 2013)

Modeling and Measuring Signal Relay in Noisy Directed Migration of Cell Groups. - Can Guven; Erin Rericha; Edward Ott; Wolfgang Losert (PLoS Comput. Biol., 2013)

The Impact of Imperfect Information on Network Attack. - Andrew Melchionna; Jesus Caloca; Shane Squires; Thomas M. Antonsen Jr.; Edward Ott; Michelle Girvan (CoRR, 2014)

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)

Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links. - Amitava Banerjee; Jaideep Pathak; Rajarshi Roy 004; Juan G. Restrepo; Edward Ott (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)

Link inference of noisy delay-coupled networks: Machine learning and opto-electronic experimental tests. - Amitava Banerjee; Joseph D. Hart; Rajarshi Roy 004; 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)

Parallel Machine Learning for Forecasting the Dynamics of Complex Networks. - Keshav Srinivasan; Nolan Coble; Joy Hamlin; Thomas M. Antonsen Jr.; Edward Ott; Michelle Girvan (CoRR, 2021)

Reservoir Computing for Forecasting Large Spatiotemporal Dynamical Systems. - Jaideep Pathak; Edward Ott (Reservoir Computing, 2021)

Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systems. - Dhruvit Patel; Edward Ott (CoRR, 2022)

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)