Zobrazeno 1 - 10
of 227
pro vyhledávání: '"Hunt, Brian R."'
Autor:
Wikner, Alexander, Harvey, Joseph, Girvan, Michelle, Hunt, Brian R., Pomerance, Andrew, Antonsen, Thomas, Ott, Edward
Recent work has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of unknown chaotic dynamical systems. Short-term predictions of the state evolution and long-term predictions of the statistical patterns of th
Externí odkaz:
http://arxiv.org/abs/2211.05262
Autor:
Wikner, Alexander, Pathak, Jaideep, Hunt, Brian R., Szunyogh, Istvan, Girvan, Michelle, Ott, Edward
We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is in the form of noisy partial measurements of the past and present state of the dynamical system. Recently there have been several promising d
Externí odkaz:
http://arxiv.org/abs/2102.07819
Autor:
Wikner, Alexander, Harvey, Joseph, Girvan, Michelle, Hunt, Brian R., Pomerance, Andrew, Antonsen, Thomas, Ott, Edward
Publikováno v:
In Neural Networks February 2024 170:94-110
Autor:
Vlachas, Pantelis R., Pathak, Jaideep, Hunt, Brian R., Sapsis, Themistoklis P., Girvan, Michelle, Ott, Edward, Koumoutsakos, Petros
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 architect
Externí odkaz:
http://arxiv.org/abs/1910.05266
A machine-learning approach called "reservoir computing" has been used successfully for short-term prediction and attractor reconstruction of chaotic dynamical systems from time series data. We present a theoretical framework that describes condition
Externí odkaz:
http://arxiv.org/abs/1805.03362
We use recent advances in the machine learning area known as 'reservoir computing' to formulate a method for model-free estimation from data of the Lyapunov exponents of a chaotic process. The technique uses a limited time series of measurements as i
Externí odkaz:
http://arxiv.org/abs/1710.07313