88 citations · 876 across the 55 of their papers we have counts for
9 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…
Stability analysis of chaotic systems from data
Georgios Margazoglou, Luca Magri
The prediction of the temporal dynamics of chaotic systems is challenging because infinitesimal perturbations grow exponentially. The analysis of the dynamics of infinitesimal pert…
A physics-based domain adaptation framework for modelling and forecasting building energy systems
Zack Xuereb Conti, Ruchi Choudhary, Luca Magri
State-of-the-art machine-learning-based models are a popular choice for modeling and forecasting energy behavior in buildings because given enough data, they are good at finding sp…
Compositional noise in nozzles with dissipation
Animesh Jain, Luca Magri
We propose a physical model to predict indirect noise generated by the acceleration of compositional inhomogeneities in nozzles with viscous dissipation (non-isentropic nozzles). F…
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…