Structure preserving schemes for nonlinear Fokker-Planck equations with anisotropic diffusion

Nadia Loy, Mattia Zanella

Preprint arXiv, 2019.

In this work we propose novel numerical schemes for nonlinear Fokker-Planck-type equations with anisotropic diffusion matrix that preserve fundamental structural properties like non negativity of the solution, entropy dissipation and which guarantees an arbitrarily accurate approximation of the steady state of the problem. All the methods presented are at least second order accurate in the transient regimes and high order for large times. Applications of the schemes to models for collective phenomena and life sciences are considered, in these examples anomalous diffusion is often observed and must be taken into account in realistic models.

Multiple-interaction kinetic modelling of a virtual-item gambling economy

Giuseppe Toscani, Andrea Tosin, Mattia Zanella

Preprint arXiv, 2019.

In recent years, there has been a proliferation of online gambling sites, which made gambling more accessible with a consequent rise in related problems, such as addiction. Hence, the analysis of the gambling behaviour at both the individual and the aggregate levels has become the object of several investigations. In this paper, resorting to classical methods of the kinetic theory, we describe the behaviour of a multi-agent system of gamblers participating in lottery-type games on a virtual-item gambling market. The comparison with previous, often empirical, results highlights the ability of the kinetic approach to explain how the simple microscopic rules of a gambling-type game produce complex collective trends, which might be difficult to interpret precisely by looking only at the available data.


Uncertainty damping in kinetic traffic models by driver-assist controls

Andrea Tosin, Mattia Zanella

Preprint arXiv, 2019.

In this paper, we propose a kinetic model of traffic flow with uncertain binary interactions, which explains the scattering of the fundamental diagram in terms of the macroscopic variability of aggregate quantities, such as the mean speed and the flux of the vehicles, produced by the microscopic uncertainty. Moreover, we design control strategies at the level of the microscopic interactions among the vehicles, by which we prove that it is possible to dampen the propagation of such an uncertainty across the scales. Our analytical and numerical results suggest that the aggregate traffic flow may be made more ordered, hence predictable, by implementing such control protocols in driver-assist vehicles. Remarkably, they also provide a precise relationship between a measure of the macroscopic damping of the uncertainty and the penetration rate of the driver-assist technology in the traffic stream.


Monte Carlo gPC methods for diffusive kinetic flocking models with uncertainties

José Antonio Carrillo, Mattia Zanella

Preprint arXiv, 2019.

In this paper we introduce and discuss numerical schemes for the approximation of kinetic equations for flocking behavior with phase transitions that incorporate uncertain quantities. This class of schemes here considered make use of a Monte Carlo approach in the phase space coupled with a stochastic Galerkin expansion in the random space. The proposed methods naturally preserve the positivity of the statistical moments of the solution and are capable to achieve high accuracy in the random space. Several tests on a kinetic alignment model with self propulsion validate the proposed methods both in the homogeneous and inhomogeneous setting, shading light on the influence of uncertainties in phase transition phenomena driven by noise such as their smoothing and confidence bands.


Structure preserving stochastic Galerkin methods for Fokker-Planck equations with background interactions

Mattia Zanella

Preprint arXiv, 2019.

This paper is devoted to the construction of structure preserving stochastic Galerkin schemes for Fokker-Planck type equations with uncertainties and interacting with an external distribution called the background. The proposed methods are capable to preserve physical properties in the approximation of statistical moments of the problem like nonnegativity, entropy dissipation and asymptotic behaviour of the expected solution. The introduced methods are second order accurate in the transient regimes and high order for large times. We present applications of the developed schemes to the case of fixed and dynamic background distribution for models of collective behaviour.


Hydrodynamic models of preference formation in multi-agent societies

Lorenzo Pareschi, Giuseppe Toscani, Andrea Tosin, Mattia Zanella

Preprint arXiv, 2018.

In this paper, we discuss the passage to hydrodynamic equations for kinetic models of opinion formation. The considered kinetic models feature an opinion density depending on an additional microscopic variable, identified with the personal preference. This variable describes an opinion-driven polarisation process, leading finally to a choice among some possible options, as it happens e.g. in referendums or elections. Like in the kinetic theory of rarefied gases, the derivation of hydrodynamic equations is essentially based on the computation of the local equilibrium distribution of the opinions from the underlying kinetic model. Several numerical examples validate the resulting model, shedding light on the crucial role played by the distinction between opinion and preference formation on the choice processes in multi-agent societies.

Kinetic-controlled hydrodynamics for traffic models with driver-assist vehicles

Andrea Tosin, Mattia Zanella

Multiscale Modeling and Simulation, 17(2): 716-749, 2019. Preprint arXiv, 2018.

We develop a hierarchical description of traffic flow control by means of driver-assist vehicles aimed at the mitigation of speed-dependent road risk factors. Microscopic feedback control strategies are designed at the level of vehicle-to-vehicle interactions and then upscaled to the global flow via a kinetic approach based on a Boltzmann-type equation. Then first and second order hydrodynamic traffic models, which naturally embed the microscopic control strategies, are consistently derived from the kinetic-controlled framework via suitable closure methods. Several numerical examples illustrate the effectiveness of such a hierarchical approach at the various scales.


Opinion modeling on social media and marketing aspects

Giuseppe Toscani, Andrea Tosin, Mattia Zanella

Physical Review E, 98(2): 022315, 2018. (Preprint arXiv)

We introduce and discuss kinetic models of opinion formation on social networks in which the distribution function depends on both the opinion and the connectivity of the agents. The opinion formation model is subsequently coupled with a kinetic model describing the spreading of popularity of a product on the web through a social network. Numerical experiments on the underlying kinetic models show a good qualitative agreement with some measured trends of hashtags on social media websites and illustrate how companies can take advantage of the network structure to obtain at best the advertisement of their products.

Related popularization article for the Italian blog Madd:Math!: La popolarità delle opinioni



Boltzmann games in heterogeneous consensus dynamics

Giacomo Albi, Lorenzo Pareschi, Mattia Zanella

Journal of Statistical Physics, 175(1): 97–125, 2019. (Preprint arXiv)

We consider a constrained hierarchical opinion dynamics in the case of leaders’ competition and with complete information among leaders. Each leaders’ group tries to drive the followers’ opinion towards a desired state accordingly to a specific strategy. By using the Boltzmann-type control approach we analyze the best-reply strategy for each leaders’ population. Derivation of the corresponding Fokker-Planck model permits to investigate the asymptotic behaviour of the solution. Heterogeneous followers populations are then considered where the effect of knowledge impacts the leaders’ credibility and modifies the outcome of the leaders’ competition.

Particle based gPC methods for mean-field models of swarming with uncertainty

José Antonio Carrillo, Lorenzo Pareschi, Mattia Zanella.

Communications in Computational Physics, 25(2): 508-531, 2019. (Preprint arXiv)

In this work we focus on the construction of numerical schemes for the approximation of stochastic mean–field equations which preserve the nonnegativity of the solution. The method here developed makes use of a mean-field Monte Carlo method in the physical variables combined with a generalized Polynomial Chaos (gPC) expansion in the random space. In contrast to a direct application of stochastic-Galerkin methods, which are highly accurate but lead to the loss of positivity, the proposed schemes are capable to achieve high accuracy in the random space without loosing nonnegativity of the solution. Several applications of the schemes to mean-field models of collective behavior are reported.


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