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Chaos (Woodbury, N.Y.)|January 1, 2018
Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from dataJaideep Pathak, Zhixin Lu, Brian R Hunt, et al.Physical Review Letters|January 30, 2018
Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing ApproachJaideep Pathak, Brian Hunt, Michelle Girvan, et al.Chaos (Woodbury, N.Y.)|May 1, 2017
Reservoir observers: Model-free inference of unmeasured variables in chaotic systemsZhixin Lu, Jaideep Pathak, Brian Hunt, et al.Chaos (Woodbury, N.Y.)|July 9, 2021
Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based componentsAlexander Wikner, Jaideep Pathak, Brian R Hunt, et al.Chaos (Woodbury, N.Y.)|January 8, 2020
Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based modelJaideep Pathak, Alexander Wikner, Rebeckah Fussell, et al.Physical Review. E|January 14, 2017
Inhibitory neurons promote robust critical firing dynamics in networks of integrate-and-fire neuronsZhixin Lu, Shane Squires, Edward Ott, et al.Chaos (Woodbury, N.Y.)|March 2, 2020
Separation of chaotic signals by reservoir computingSanjukta Krishnagopal, Michelle Girvan, Edward Ott, et al.Chaos (Woodbury, N.Y.)|July 2, 2018
Attractor reconstruction by machine learningZhixin Lu, Brian R Hunt, Edward OttChaos (Woodbury, N.Y.)|June 4, 2020
Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systemsAlexander Wikner, Jaideep Pathak, Brian Hunt, et al.Neural Networks : the Official Journal of the International Neural Network Society|November 17, 2023
Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computingAlexander Wikner, Joseph Harvey, Michelle Girvan, et al.Pageof 20