88 citations · 876 across the 55 of their papers we have counts for
15 papers · 1 filter
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…
Super-resolving sparse observations in partial differential equations: A physics-constrained convolutional neural network approach
Daniel Kelshaw, Luca Magri
We propose the physics-constrained convolutional neural network (PC-CNN) to infer the high-resolution solution from sparse observations of spatiotemporal and nonlinear partial diff…
Uncovering solutions from data corrupted by systematic errors: A physics-constrained convolutional neural network approach
Daniel Kelshaw, Luca Magri
Information on natural phenomena and engineering systems is typically contained in data. Data can be corrupted by systematic errors in models and experiments. In this paper, we pro…
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…
Data-driven modelling for drop size distributions
Tullio Traverso, Thomas Abadie, Omar K. Matar +1
The prediction of the drop size distribution (DSD) resulting from liquid atomization is key to the optimization of multi-phase flows, from gas-turbine propulsion, through agricultu…
A machine learning approach to the prediction of heat-transfer coefficients in micro-channels
Tullio Traverso, Francesco Coletti, Luca Magri +2
The accurate prediction of the two-phase heat transfer coefficient (HTC) as a function of working fluids, channel geometries and process conditions is key to the optimal design and…
Short and Straight: Geodesics on Differentiable Manifolds
Daniel Kelshaw, Luca Magri
Manifolds discovered by machine learning models provide a compact representation of the underlying data. Geodesics on these manifolds define locally length-minimising curves and pr…
Hard-constrained neural networks for modelling nonlinear acoustics
Defne Ege Ozan, Luca Magri
We model acoustic dynamics in space and time from synthetic sensor data. The tasks are (i) to predict and extrapolate the spatiotemporal dynamics, and (ii) reconstruct the acoustic…
Reconstruction, forecasting, and stability of chaotic dynamics from partial data
Elise Özalp, Georgios Margazoglou, Luca Magri
The forecasting and computation of the stability of chaotic systems from partial observations are tasks for which traditional equation-based methods may not be suitable. In this co…
Quantum Multi-Model Fitting
Matteo Farina, Luca Magri, Willi Menapace +3
Geometric model fitting is a challenging but fundamental computer vision problem. Recently, quantum optimization has been shown to enhance robust fitting for the case of a single m…
Data-driven stability analysis of a chaotic time-delayed system
Georgios Margazoglou, Luca Magri
Systems with time-delayed chaotic dynamics are common in nature, from control theory to aeronautical propulsion. The overarching objective of this paper is to compute the stability…
Physics-Informed Long Short-Term Memory for Forecasting and Reconstruction of Chaos
Elise Özalp, Georgios Margazoglou, Luca Magri
We present the Physics-Informed Long Short-Term Memory (PI-LSTM) network to reconstruct and predict the evolution of unmeasured variables in a chaotic system. The training is const…
Bayesian optimization of the layout of wind farms with a high-fidelity surrogate model
Nikolaos Bempedelis, Luca Magri
We introduce a gradient-free data-driven framework for optimizing the power output of a wind farm based on a Bayesian approach and large-eddy simulations. In contrast with conventi…
Indirect noise from weakly reacting inhomogeneities
Animesh Jain, Andrea Giusti, Luca Magri
Indirect noise is a significant contributor to aircraft engine noise, which needs to be minimized in the design of aircraft engines. Indirect noise is caused by the acceleration of…
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…