81 citations · 589 across the 46 of their papers we have counts for
5 papers · 2 filters
Asymptotic preserving methods for the low mach limit in discrete velocity models approximating kinetic equations
Giacomo Dimarco, Axel Klar, Theresa Köfler +1
We consider a Lattice Boltzmann type discrete velocity model in the low Mach number scaling and develop a corresponding numerical scheme that remains uniformly valid across all reg…
Augmented data and neural networks for robust epidemic forecasting: application to COVID-19 in Italy
Giacomo Dimarco, Federica Ferrarese, Lorenzo Pareschi
In this work, we propose a data augmentation strategy aimed at improving the training phase of neural networks and, consequently, the accuracy of their predictions. Our approach re…
Structure and asymptotic preserving deep neural surrogates for uncertainty quantification in multiscale kinetic equations
Wei Chen, Giacomo Dimarco, Lorenzo Pareschi
The high dimensionality of kinetic equations with stochastic parameters poses major computational challenges for uncertainty quantification (UQ). Traditional Monte Carlo (MC) sampl…
A structure and asymptotic preserving scheme for the quasineutral limit of the Vlasov-Poisson system
Alain Blaustein, Giacomo Dimarco, Francis Filbet +1
In this work, we propose a new numerical method for the Vlasov-Poisson system that is both asymptotically consistent and stable in the quasineutral regime, i.e. when the Debye leng…
A data augmentation strategy for deep neural networks with application to epidemic modelling
Muhammad Awais, Abu Safyan Ali, Giacomo Dimarco +2
In this work, we integrate the predictive capabilities of compartmental disease dynamics models with machine learning ability to analyze complex, high-dimensional data and uncover…