# Lorenzo Pareschi

## Thursday, October 12, 2023

### Kinetic description and convergence analysis of genetic algorithms for global optimization

## Saturday, August 5, 2023

### Gradient-based Monte Carlo methods for relaxation approximations of hyperbolic conservation laws

## Wednesday, June 14, 2023

### Particle simulation methods for the Landau-Fokker-Planck equation with uncertain data

The design of particle simulation methods for collisional plasma physics has always represented a challenge due to the unbounded total collisional cross section, which prevents a natural extension of the classical Direct Simulation Monte Carlo (DSMC) method devised for the Boltzmann equation. One way to overcome this problem is to consider the design of Monte Carlo algorithms that are robust in the so-called grazing collision limit. In the first part of this manuscript, we will focus on the construction of collision algorithms for the Landau-Fokker-Planck equation based on the grazing collision asymptotics and which avoids the use of iterative solvers.

## Saturday, February 18, 2023

### Multiscale constitutive framework of 1D blood flow modeling: asymptotic limits and numerical methods

*SIAM Multiscale Modeling and Simulation*21(3), 1237-126, 2023. Preprint arXiv:2302.09374)

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 shown that different types of blood propagation phenomena in arteries and veins can be described through an appropriate choice of scaling parameters, which are related to distinct characterizations of the fluid-structure interaction mechanism (whether elastic or viscoelastic) that exist between vessel walls and blood flow. In these asymptotic limits, well-known blood flow models from the literature are recovered. Additionally, by analyzing the perturbation of the local elastic equilibrium of the system, a new viscoelastic blood flow model is derived.

## Thursday, February 2, 2023

### Modeling opinion polarization on social media: application to Covid-19 vaccination hesitancy in Italy

*PLoS ONE,*18(10):e0291993, 2023)

The SARS-CoV-2 pandemic reminded us how vaccination can be a divisive topic on which the public conversation is permeated by misleading claims, and thoughts tend to polarize, especially on online social networks. In this work, motivated by recent natural language processing techniques to systematically extract and quantify opinions from text messages, we present a differential framework for bivariate opinion formation dynamics that is coupled with a compartmental model for fake news dissemination.