717 research outputs found
Binary interaction algorithms for the simulation of flocking and swarming dynamics
Microscopic models of flocking and swarming takes in account large numbers of
interacting individ- uals. Numerical resolution of large flocks implies huge
computational costs. Typically for interacting individuals we have a cost
of . We tackle the problem numerically by considering approximated
binary interaction dynamics described by kinetic equations and simulating such
equations by suitable stochastic methods. This approach permits to compute
approximate solutions as functions of a small scaling parameter
at a reduced complexity of O(N) operations. Several numerical results show the
efficiency of the algorithms proposed
Uncertainty quantification for kinetic models in socio-economic and life sciences
Kinetic equations play a major rule in modeling large systems of interacting
particles. Recently the legacy of classical kinetic theory found novel
applications in socio-economic and life sciences, where processes characterized
by large groups of agents exhibit spontaneous emergence of social structures.
Well-known examples are the formation of clusters in opinion dynamics, the
appearance of inequalities in wealth distributions, flocking and milling
behaviors in swarming models, synchronization phenomena in biological systems
and lane formation in pedestrian traffic. The construction of kinetic models
describing the above processes, however, has to face the difficulty of the lack
of fundamental principles since physical forces are replaced by empirical
social forces. These empirical forces are typically constructed with the aim to
reproduce qualitatively the observed system behaviors, like the emergence of
social structures, and are at best known in terms of statistical information of
the modeling parameters. For this reason the presence of random inputs
characterizing the parameters uncertainty should be considered as an essential
feature in the modeling process. In this survey we introduce several examples
of such kinetic models, that are mathematically described by nonlinear Vlasov
and Fokker--Planck equations, and present different numerical approaches for
uncertainty quantification which preserve the main features of the kinetic
solution.Comment: To appear in "Uncertainty Quantification for Hyperbolic and Kinetic
Equations
Refining self-propelled particle models for collective behaviour
Swarming, schooling, flocking and herding are all names given to the wide variety of collective behaviours exhibited by groups of animals, bacteria and even individual cells. More generally, the term swarming describes the behaviour of an aggregate of agents (not necessarily biological) of similar size and shape which exhibit some emergent property such as directed migration or group cohesion. In this paper we review various individual-based models of collective behaviour and discuss their merits and drawbacks. We further analyse some one-dimensional models in the context of locust swarming. In specific models, in both one and two dimensions, we demonstrate how varying the parameters relating to how much attention individuals pay to their neighbours can dramatically change the behaviour of the group. We also introduce leader individuals to these models with the ability to guide the swarm to a greater or lesser degree as we vary the parameters of the model. We consider evolutionary scenarios for models with leaders in which individuals are allowed to evolve the degree of influence neighbouring individuals have on their subsequent motion
Efficient numerical calculation of drift and diffusion coefficients in the diffusion approximation of kinetic equations
In this paper we study the diffusion approximation of a swarming model given
by a system of interacting Langevin equations with nonlinear friction. The
diffusion approximation requires the calculation of the drift and diffusion
coefficients that are given as averages of solutions to appropriate Poisson
equations. We present a new numerical method for computing these coefficients
that is based on the calculation of the eigenvalues and eigenfunctions of a
Schr\"odinger operator. These theoretical results are supported by numerical
simulations showcasing the efficiency of the method
Kinetic description of optimal control problems and applications to opinion consensus
In this paper an optimal control problem for a large system of interacting
agents is considered using a kinetic perspective. As a prototype model we
analyze a microscopic model of opinion formation under constraints. For this
problem a Boltzmann-type equation based on a model predictive control
formulation is introduced and discussed. In particular, the receding horizon
strategy permits to embed the minimization of suitable cost functional into
binary particle interactions. The corresponding Fokker-Planck asymptotic limit
is also derived and explicit expressions of stationary solutions are given.
Several numerical results showing the robustness of the present approach are
finally reported.Comment: 25 pages, 18 figure
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