Search Results for author:
Found 11 papers, 1 papers with code
Modelling contagious viral dynamics: a kinetic approach based on mutual utility
The temporal evolution of a contagious viral disease is modelled as the dynamic progression of different classes of population with individuals interacting pairwise.
The kinetic theory of mutation rates
The Luria--Delbr\"uck mutation model is a cornerstone of evolution theory and has been mathematically formulated in a number of ways.
Asymptotic-Preserving Neural Networks for multiscale hyperbolic models of epidemic spread
To allow the neural network to operate uniformly with respect to the small scales, it is desirable that the neural network satisfies an Asymptotic-Preservation (AP) property in the learning process.
Kinetic modelling of epidemic dynamics: social contacts, control with uncertain data, and multiscale spatial dynamics
In this survey we report some recent results in the mathematical modeling of epidemic phenomena through the use of kinetic equations.
Spatial spread of COVID-19 outbreak in Italy using multiscale kinetic transport equations with uncertainty
Because of the high uncertainty in the data reported in the early phase of the epidemic, the presence of random inputs in both the initial data and the epidemic parameters is included in the model.
Modelling lockdown measures in epidemic outbreaks using selective socio-economic containment with uncertainty
After the introduction of drastic containment measures aimed at stopping the epidemic contagion from SARS-CoV2, many governments have adopted a strategy based on a periodic relaxation of such measures in the face of a severe economic crisis caused by lockdowns.
Hyperbolic compartmental models for epidemic spread on networks with uncertain data: application to the emergence of Covid-19 in Italy
The importance of spatial networks in the spread of an epidemic is an essential aspect in modeling the dynamics of an infectious disease.
Mean-field control variate methods for kinetic equations with uncertainties and applications to socio-economic sciences
In this paper, we extend a recently introduced multi-fidelity control variate for the uncertainty quantification of the Boltzmann equation to the case of kinetic models arising in the study of multiagent systems.
Numerical Analysis Statistical Mechanics Numerical Analysis Adaptation and Self-Organizing Systems
From particle swarm optimization to consensus based optimization: stochastic modeling and mean-field limit
A regularization process for the global best permits to formally derive the respective mean-field description.
Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning
To quantify the performances of the new approach, we show that the algorithm is able to perform essentially as good as ad hoc state of the art methods in challenging problems in signal processing and machine learning, namely the phase retrieval problem and the robust subspace detection.
Consensus-Based Optimization on Hypersurfaces: Well-Posedness and Mean-Field Limit
We introduce a new stochastic differential model for global optimization of nonconvex functions on compact hypersurfaces.
