Search Results for author: Edward Ott

Found 12 papers, 3 papers with code

Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization

1 code implementation9 Nov 2022 Alexander Wikner, Joseph Harvey, Michelle Girvan, Brian R. Hunt, Andrew Pomerance, Thomas Antonsen, Edward Ott

In this article, we systematically examine the technique of adding noise to the ML model input during training to promote stability and improve prediction accuracy.

Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systems

no code implementations1 Jul 2022 Dhruvit Patel, Edward Ott

We focus on the particularly challenging situation where the past dynamical state time series that is available for ML training predominantly lies in a restricted region of the state space, while the behavior to be predicted evolves on a larger state space set not fully observed by the ML model during training.

Hyperparameter Optimization Time Series

Short-wavelength Reverberant Wave Systems for Physical Realization of Reservoir Computing

no code implementations13 Apr 2022 Shukai Ma, Thomas M. Antonsen, Steven M. Anlage, Edward Ott

Machine learning (ML) has found widespread application over a broad range of important tasks.

Parallel Machine Learning for Forecasting the Dynamics of Complex Networks

no code implementations27 Aug 2021 Keshav Srinivasan, Nolan Coble, Joy Hamlin, Thomas Antonsen, Edward Ott, Michelle Girvan

Forecasting the dynamics of large complex networks from previous time-series data is important in a wide range of contexts.

BIG-bench Machine Learning Time Series

Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components

no code implementations15 Feb 2021 Alexander Wikner, Jaideep Pathak, Brian R. Hunt, Istvan Szunyogh, Michelle Girvan, Edward Ott

We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.

BIG-bench Machine Learning

Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests

no code implementations29 Oct 2020 Amitava Banerjee, Joseph D. Hart, Rajarshi Roy, Edward Ott

To achieve this, we first train a type of machine learning system known as reservoir computing to mimic the dynamics of the unknown network.

BIG-bench Machine Learning Time Series

Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems

no code implementations10 Feb 2020 Alexander Wikner, Jaideep Pathak, Brian Hunt, Michelle Girvan, Troy Arcomano, Istvan Szunyogh, Andrew Pomerance, Edward Ott

We consider the commonly encountered situation (e. g., in weather forecasting) where the goal is to predict the time evolution of a large, spatiotemporally chaotic dynamical system when we have access to both time series data of previous system states and an imperfect model of the full system dynamics.

BIG-bench Machine Learning Time Series +1

Separation of Chaotic Signals by Reservoir Computing

1 code implementation18 Oct 2019 Sanjukta Krishnagopal, Michelle Girvan, Edward Ott, Brian Hunt

Indeed, our method works well when the component frequency spectra are indistinguishable - a case where a Wiener filter performs essentially no separation.

Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics

1 code implementation9 Oct 2019 Pantelis R. Vlachas, Jaideep Pathak, Brian R. Hunt, Themistoklis P. Sapsis, Michelle Girvan, Edward Ott, Petros Koumoutsakos

We examine the efficiency of Recurrent Neural Networks in forecasting the spatiotemporal dynamics of high dimensional and reduced order complex systems using Reservoir Computing (RC) and Backpropagation through time (BPTT) for gated network architectures.

Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model

no code implementations9 Mar 2018 Jaideep Pathak, Alexander Wikner, Rebeckah Fussell, Sarthak Chandra, Brian Hunt, Michelle Girvan, Edward Ott

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the physical processes governing the dynamics to build an approximate mathematical model of the system.

BIG-bench Machine Learning Time Series +1

Using Machine Learning to Replicate Chaotic Attractors and Calculate Lyapunov Exponents from Data

no code implementations19 Oct 2017 Jaideep Pathak, Zhixin Lu, Brian R. Hunt, Michelle Girvan, Edward Ott

For the case of the KS equation, we note that as the system's spatial size is increased, the number of Lyapunov exponents increases, thus yielding a challenging test of our method, which we find the method successfully passes.

Chaotic Dynamics

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