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Found 7 papers, 1 papers with code
Control-aware echo state networks (Ca-ESN) for the suppression of extreme events
Extreme event are sudden large-amplitude changes in the state or observables of chaotic nonlinear systems, which characterize many scientific phenomena.
Convolutional autoencoder for the spatiotemporal latent representation of turbulence
The goal of this paper is to obtain an efficient and accurate reduced-order latent representation of a turbulent flow that exhibits extreme events.
Modelling spatiotemporal turbulent dynamics with the convolutional autoencoder echo state network
The overarching objective of this paper is to propose a nonlinear decomposition of the turbulent state for a reduced-order representation of the dynamics.
Data-driven prediction and control of extreme events in a chaotic flow
We show that echo state networks are able to predict extreme events well beyond the predictability time, i. e., up to more than five Lyapunov times.
Statistical prediction of extreme events from small datasets
We asses whether the networks are able to extrapolate from the small imperfect datasets and predict the heavy-tail statistics that describe the events.
Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics
The proposed validation strategies, which are based on the dynamical systems properties of chaotic time series, are shown to outperform the state-of-the-art validation strategies.
Automatic-differentiated Physics-Informed Echo State Network (API-ESN)
The network is showcased in the reconstruction of unmeasured (hidden) states of a chaotic system.
