L
Lorenzo Pareschi
Researcher at University of Ferrara
Publications - 247
Citations - 8918
Lorenzo Pareschi is an academic researcher from University of Ferrara. The author has contributed to research in topics: Boltzmann equation & Monte Carlo method. The author has an hindex of 45, co-authored 236 publications receiving 7402 citations. Previous affiliations of Lorenzo Pareschi include University of Wisconsin-Madison & Union des Industries Ferroviaires Européennes.
Papers
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Relaxing lockdown measures in epidemic outbreaks using selective socio-economic containment with uncertainty
TL;DR: Starting from a compartmental model with a social structure, models with multiple feedback controls depending on the social activities that allow to assess the impact of a selective relaxation of the containment measures in the presence of uncertain data are derived.
Posted Content
Binary interaction methods for high dimensional global optimization and machine learning
TL;DR: In this article, a new class of gradient-free global optimization methods based on a binary interaction dynamics governed by a Boltzmann type equation is introduced, where the particles act taking into account both the best microscopic binary position and the best macroscopic collective position.
Posted Content
Exponential Runge-Kutta schemes for inhomogeneous Boltzmann equations with high order of accuracy
Qin Li,Lorenzo Pareschi +1 more
TL;DR: It is shown how to derive asymptotic preserving (AP) schemes of arbitrary order using the Shu-Osher representation of Runge-Kutta methods and the monotonicity properties of such schemes, like strong stability preserving (SSP) and positivity preserving are explored.
Posted Content
On the optimal control of opinion dynamics on evolving networks
TL;DR: In this paper, a mathematical model is formulated as a coupling of an opinion alignment system with a probabilistic description of the network, and a control strategy based on the degree of connection of each agent is designed.
Book ChapterDOI
On the Optimal Control of Opinion Dynamics on Evolving Networks
TL;DR: The results show that in this work it is possible to drive the overall opinion toward a desired state even if the authors control only a suitable fraction of the nodes.