39 citations · 63 across the 14 of their papers we have counts for
14 papers
Adjoint Sensitivities of Chaotic Flows without Adjoint Solvers: A Data-Driven Approach
Defne E. Ozan, Luca Magri
In one calculation, adjoint sensitivity analysis provides the gradient of a quantity of interest with respect to all system's parameters. Conventionally, adjoint solvers need to be…
Computing distances and means on manifolds with a metric-constrained Eikonal approach
Daniel Kelshaw, Luca Magri
Computing distances on Riemannian manifolds is a challenging problem with numerous applications, from physics, through statistics, to machine learning. In this paper, we introduce…
Solving nonlinear differential equations on Quantum Computers: A Fokker-Planck approach
Felix Tennie, Luca Magri
For quantum computers to become useful tools to physicists, engineers and computational scientists, quantum algorithms for solving nonlinear differential equations need to be devel…
Manifold-augmented Eikonal Equations: Geodesic Distances and Flows 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…
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…
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…