10 papers
Control variates with neural surrogates for uncertainty quantification in kinetic equations
Wei Chen, Giacomo Dimarco, Lorenzo Pareschi
Efficient uncertainty quantification for kinetic equations with random inputs is challenging because it requires repeated simulations of high-dimensional models, such as the Boltzm…
High-Order Asymptotic-Preserving Schemes for Kinetic Equations from Rarefied to Incompressible Regimes
Giacomo Dimarco, Axel Klar, Theresa Köfler +2
This work introduces a novel high-order numerical framework for solving kinetic equations, designed to remain uniformly valid across all regimes of the mean free path, spanning fro…
How opinions shape epidemics: a graphon-based kinetic approach
Abu Safyan Ali, Elisa Calzola, Giacomo Dimarco +2
Understanding the mutual influence between social behavior and physical health is crucial for designing effective epidemic mitigation strategies. Individual interactions drive the…
Micro-Macro Tensor Neural Surrogates for Uncertainty Quantification in Collisional Plasma
Wei Chen, Giacomo Dimarco, Lorenzo Pareschi
Plasma kinetic equations exhibit pronounced sensitivity to microscopic perturbations in model parameters and data, making reliable and efficient uncertainty quantification (UQ) ess…
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…