80 citations · 313 across the 22 of their papers we have counts for
17 papers · 1 filter
On interpretability and proper latent decomposition of autoencoders
Luca Magri, Anh Khoa Doan
The dynamics of a turbulent flow tend to occupy only a portion of the phase space at a statistically stationary regime. From a dynamical systems point of view, this portion is the…
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…
Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems
Daniel Kelshaw, Georgios Rigas, Luca Magri
In the absence of high-resolution samples, super-resolution of sparse observations on dynamical systems is a challenging problem with wide-reaching applications in experimental set…
Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems
Daniel Kelshaw, Luca Magri
Measurements on dynamical systems, experimental or otherwise, are often subjected to inaccuracies capable of introducing corruption; removal of which is a problem of fundamental im…
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…
Short- and long-term prediction of a chaotic flow: A physics-constrained reservoir computing approach
Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
We propose a physics-constrained machine learning method-based on reservoir computing- to time-accurately predict extreme events and long-term velocity statistics in a model of tur…