This paper presents a critical analysis of the literature and perspective research ideas for
modeling the epidemics caused by the SARS-CoV-2 virus. It goes beyond deterministic …

When investigating epidemic dynamics through differential models, the parameters needed
to understand the phenomenon and to simulate forecast scenarios require a delicate …

In this paper, we focus on the construction of a hybrid scheme for the approximation of non-
Maxwellian kinetic models with uncertainties. In the context of multiagent systems, the …

Excerpt This book focuses on IMEX methods, with particular emphasis on their application to
systems of PDEs. IMEX methods have proven to be highly effective for solving a wide range …

The time evolution of a contagious viral disease is modeled as the dynamic progression of
different classes of populations that interact pairwise, aiming to improve their condition with …

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 …

The construction of efficient methods for uncertainty quantification in kinetic equations
represents a challenge due to the high dimensionality of the models: often the computational …

In this paper, we present an asymptotic preserving scheme for the two-dimensional space-
dependent and multi-scale kinetic SIR epidemic model which is widely used to model the …

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 is …

In traffic flow modeling, incorporating uncertainty is crucial for accurately capturing the
complexities of real-world scenarios. In this work we focus on kinetic models of traffic flow …