DCDS-B

A stochastic differential equation with an a.s. locally stable
compact set is considered. The attraction probabilities to the set
are characterized by the sublevel sets of the limit of a sequence
of solutions to $2^{nd}$ order partial differential equations. Two
numerical examples illustrating the method are presented.

DCDS

Exploiting the metric approach to Hamilton-Jacobi equation
recently introduced by Fathi and Siconolfi [13], we prove a
singular perturbation result for a general class of
Hamilton-Jacobi equations. Considered in the framework of small
random perturbations of dynamical systems, it extends a result due
to Kamin [19] to the case of a dynamical system having
several attracting points inside the domain.

NHM

In this article we consider a model first order mean field game problem, introduced by J.M. Lasry and P.L. Lions in [18]. Its solution $(v,m)$ can be obtained as the limit of the solutions of the second order mean field game problems, when the *noise* parameter tends to zero (see [18]). We propose a semi-discrete in time approximation of the system and, under natural assumptions, we prove that it is well posed and that it converges to $(v,m)$ when the discretization parameter tends to zero.

NHM

This paper concerns periodic multiscale homogenization for fully nonlinear equations of
the form $u^\epsilon+H^\epsilon (x,\frac{x}{\epsilon},\ldots,\frac{x}{epsilon^k},Du^\epsilon,D^2u^\epsilon)=0$.
The operators $H^\epsilon$ are a regular perturbations of some uniformly elliptic,
convex operator $H$.
As $\epsilon\to 0^+$, the solutions $u^\epsilon$ converge locally uniformly to the solution
$u$ of a suitably defined effective problem.
The purpose of this paper is to obtain an estimate of the corresponding rate of convergence.
Finally, some examples are discussed.

CPAA

Aim of this paper is to show that some of the results in the weak KAM theory
for $1^{s t}$ order convex Hamilton-Jacobi
equations (see [11], [13])
can be extended to systems of convex Hamilton-Jacobi equations with implicit obstacles and to the obstacle problem.
We obtain two results:
a comparison theorem for systems lacking strict monotonicity; a representation formula
for the obstacle problem involving the distance function
associated to the Hamiltonian of the equation.

NHM

The theory of Mean Field Games (MFG, in short) is a branch of the theory of Differential Games which aims at modeling and analyzing complex decision processes involving a large number of indistinguishable rational agents who have individually a very small influence on the overall system and are, on the other hand, influenced by the mass of the other agents.
The name comes from particle physics where it is common to consider interactions among particles as an external mean field which influences the particles. In spite of the optimization made by rational agents, playing the role of particles in such models, appropriate mean field equations can be derived to replace the many particles interactions by a single problem with an appropriately chosen external mean field which takes into account the global behavior of the individuals.

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DCDS-B

In the present article, we study the numerical approximation of a system of Hamilton-Jacobi and transport
equations arising in geometrical optics. We consider a semi-Lagrangian scheme.
We prove the well posedness of the discrete problem and the convergence of the approximated solution toward the viscosity-measure valued solution of the exact problem.

DCDS

In [14], Guéant, Lasry and Lions considered the model problem ``What time does meeting start?'' as a prototype for a general class of optimization problems with a continuum of players, called Mean Field Games problems. In this paper we consider a similar model, but with the dynamics of the agents defined on a network. We discuss appropriate transition conditions at the vertices which give a well posed problem and we present some numerical results.

NHM

In this paper we formulate a theory of measure-valued linear transport equations on networks. The building block of our approach is the initial and boundary-value problem for the measure-valued linear transport equation on a bounded interval, which is the prototype of an arc of the network. For this problem we give an explicit representation formula of the solution, which also considers the total mass flowing out of the interval. Then we construct the global solution on the network by gluing all the measure-valued solutions on the arcs by means of appropriate distribution rules at the vertexes. The measure-valued approach makes our framework suitable to deal with multiscale flows on networks, with the microscopic and macroscopic phases represented by Lebesgue-singular and Lebesgue-absolutely continuous measures, respectively, in time and space.

NHM

In this paper, we introduce a discrete time-finite state model for pedestrian flow on a graph in the spirit of the Hughes dynamic continuum model. The pedestrians, represented by a density function, move on the graph choosing a route to minimize the instantaneous travel cost to the destination. The density is governed by a conservation law whereas the minimization principle is described by a graph eikonal equation. We show that the discrete model is well-posed and the numerical examples reported confirm the validity of the proposed model and its applicability to describe real situations.