61 citations · 416 across the 21 of their papers we have counts for
8 papers · 1 filter
Multi-Order Monte Carlo IMEX hierarchies for uncertainty quantification in multiscale hyperbolic systems
Giulia Bertaglia, Walter Boscheri, Lorenzo Pareschi
We introduce a novel Multi-Order Monte Carlo approach for uncertainty quantification in the context of multiscale time-dependent partial differential equations. The new framework l…
Nonconforming Virtual Element basis functions for space-time Discontinuous Galerkin schemes on unstructured Voronoi meshes
Walter Boscheri, Giulia Bertaglia
We introduce a new class of Discontinuous Galerkin (DG) methods for solving nonlinear conservation laws on unstructured Voronoi meshes that use a nonconforming Virtual Element basi…
Gradient-based Monte Carlo methods for relaxation approximations of hyperbolic conservation laws
Giulia Bertaglia, Lorenzo Pareschi, Russel E. Caflisch
Particle methods based on evolving the spatial derivatives of the solution were originally introduced to simulate reaction-diffusion processes, inspired by vortex methods for the N…
A new family of semi-implicit Finite Volume / Virtual Element methods for incompressible flows on unstructured meshes
Walter Boscheri, Andrea Chiozzi, Michele Giuliano Carlino +1
We introduce a new family of high order accurate semi-implicit schemes for the solution of non-linear hyperbolic partial differential equations on unstructured polygonal meshes. Th…
Multiscale constitutive framework of 1D blood flow modeling: Asymptotic limits and numerical methods
Giulia Bertaglia, Lorenzo Pareschi
In this paper, a multiscale constitutive framework for one-dimensional blood flow modeling is presented and discussed. By analyzing the asymptotic limits of the proposed model, it…
Asymptotic-Preserving Neural Networks for hyperbolic systems with diffusive scaling
Giulia Bertaglia
With the rapid advance of Machine Learning techniques and the deep increase of availability of scientific data, data-driven approaches have started to become progressively popular…
Asymptotic-Preserving Neural Networks for multiscale hyperbolic models of epidemic spread
Giulia Bertaglia, Chuan Lu, Lorenzo Pareschi +1
When investigating epidemic dynamics through differential models, the parameters needed to understand the phenomenon and to simulate forecast scenarios require a delicate calibrati…
Bi-fidelity stochastic collocation methods for epidemic transport models with uncertainties
Giulia Bertaglia, Liu Liu, Lorenzo Pareschi +1
Uncertainty in data is certainly one of the main problems in epidemiology, as shown by the recent COVID-19 pandemic. The need for efficient methods capable of quantifying uncertain…