45 citations · 51 across the 7 of their papers we have counts for
8 papers
Control-aware echo state networks (Ca-ESN) for the suppression of extreme events
Alberto Racca, Luca Magri
Extreme event are sudden large-amplitude changes in the state or observables of chaotic nonlinear systems, which characterize many scientific phenomena. Because of their violent na…
Inferring unknown unknowns: Regularized bias-aware ensemble Kalman filter
Andrea Nóvoa, Alberto Racca, Luca Magri
Because of physical assumptions and numerical approximations, low-order models are affected by uncertainties in the state and parameters, and by model biases. Model biases, also kn…
Convolutional autoencoder for the spatiotemporal latent representation of turbulence
Nguyen Anh Khoa Doan, Alberto Racca, Luca Magri
Turbulence is characterised by chaotic dynamics and a high-dimensional state space, which make this phenomenon challenging to predict. However, turbulent flows are often characteri…
Modelling spatiotemporal turbulent dynamics with the convolutional autoencoder echo state network
Alberto Racca, Nguyen Anh Khoa Doan, Luca Magri
The spatiotemporal dynamics of turbulent flows is chaotic and difficult to predict. This makes the design of accurate and stable reduced-order models challenging. The overarching o…
Data-driven prediction and control of extreme events in a chaotic flow
Alberto Racca, Luca Magri
An extreme event is a sudden and violent change in the state of a nonlinear system. In fluid dynamics, extreme events can have adverse effects on the system's optimal design and op…
Statistical prediction of extreme events from small datasets
Alberto Racca, Luca Magri
We propose Echo State Networks (ESNs) to predict the statistics of extreme events in a turbulent flow. We train the ESNs on small datasets that lack information about the extreme e…
Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics
Alberto Racca, Luca Magri
An approach to the time-accurate prediction of chaotic solutions is by learning temporal patterns from data. Echo State Networks (ESNs), which are a class of Reservoir Computing, c…
Automatic-differentiated Physics-Informed Echo State Network (API-ESN)
Alberto Racca, Luca Magri
We propose the Automatic-differentiated Physics-Informed Echo State Network (API-ESN). The network is constrained by the physical equations through the reservoir's exact time-deriv…