Articles with public access mandates - Edward OttLearn more
Not available anywhere: 2
Reservoir Computing for Forecasting Large Spatiotemporal Dynamical Systems
J Pathak, E Ott
Reservoir Computing: Theory, Physical Implementations, and Applications, 117-138, 2021
Mandates: US Department of Defense
Double transition to synchronization: A generic emergent transitional behavior in large systems of coupled oscillators
J Zamora-Munt, E Ott
Europhysics Letters 98 (4), 40007, 2012
Mandates: Government of Spain
Available somewhere: 59
Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach
J Pathak, B Hunt, M Girvan, Z Lu, E Ott
Physical review letters 120 (2), 024102, 2018
Mandates: US Department of Defense
Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data
J Pathak, Z Lu, BR Hunt, M Girvan, E Ott
Chaos: An Interdisciplinary Journal of Nonlinear Science 27 (12), 2017
Mandates: US Department of Defense
Backpropagation algorithms and reservoir computing in recurrent neural networks for the forecasting of complex spatiotemporal dynamics
PR Vlachas, J Pathak, BR Hunt, TP Sapsis, M Girvan, E Ott, ...
Neural Networks 126, 191-217, 2020
Mandates: US Department of Defense
Attractor reconstruction by machine learning
Z Lu, BR Hunt, E Ott
Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (6), 2018
Mandates: US Department of Defense
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
Mandates: US National Science Foundation, US Department of Defense
Reservoir observers: Model-free inference of unmeasured variables in chaotic systems
Z Lu, J Pathak, B Hunt, M Girvan, R Brockett, E Ott
Chaos: An Interdisciplinary Journal of Nonlinear Science 27 (4), 2017
Mandates: US Department of Defense
Modeling walker synchronization on the Millennium Bridge
B Eckhardt, E Ott, SH Strogatz, DM Abrams, A McRobie
Physical Review E 75 (2), 021110, 2007
Mandates: German Research Foundation
The effect of network topology on the stability of discrete state models of genetic control
A Pomerance, E Ott, M Girvan, W Losert
Proceedings of the National Academy of Sciences 106 (20), 8209-8214, 2009
Mandates: US National Institutes of Health
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
Mandates: US Department of Defense
Inhibition causes ceaseless dynamics in networks of excitable nodes
DB Larremore, WL Shew, E Ott, F Sorrentino, JG Restrepo
Physical review letters 112 (13), 138103, 2014
Mandates: US National Institutes of Health
Continuous versus Discontinuous Transitions in the -Dimensional Generalized Kuramoto Model: Odd is Different
S Chandra, M Girvan, E Ott
Physical Review X 9 (1), 011002, 2019
Mandates: US Department of Defense
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
Mandates: US Department of Defense
Resynchronization of circadian oscillators and the east-west asymmetry of jet-lag
Z Lu, K Klein-Cardeña, S Lee, TM Antonsen, M Girvan, E Ott
Chaos: An Interdisciplinary Journal of Nonlinear Science 26 (9), 2016
Mandates: US National Science Foundation
Effects of network topology, transmission delays, and refractoriness on the response of coupled excitable systems to a stochastic stimulus
DB Larremore, WL Shew, E Ott, JG Restrepo
Chaos: An Interdisciplinary Journal of Nonlinear Science 21 (2), 2011
Mandates: US National Institutes of Health
Using machine learning to predict statistical properties of non-stationary dynamical processes: System climate, regime transitions, and the effect of stochasticity
D Patel, D Canaday, M Girvan, A Pomerance, E Ott
Chaos: An Interdisciplinary Journal of Nonlinear Science 31 (3), 2021
Mandates: US National Science Foundation, US Department of Defense
Separation of chaotic signals by reservoir computing
S Krishnagopal, M Girvan, E Ott, BR Hunt
Chaos: An Interdisciplinary Journal of Nonlinear Science 30 (2), 2020
Mandates: US National Science Foundation, US Department of Defense
Modeling the network dynamics of pulse-coupled neurons
S Chandra, D Hathcock, K Crain, TM Antonsen, M Girvan, E Ott
Chaos: An Interdisciplinary Journal of Nonlinear Science 27 (3), 2017
Mandates: US National Science Foundation, US Department of Defense
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
Mandates: US National Science Foundation, US Department of Defense
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