24,570 research outputs found
Statistics of non-linear stochastic dynamical systems under L\'evy noises by a convolution quadrature approach
This paper describes a novel numerical approach to find the statistics of the
non-stationary response of scalar non-linear systems excited by L\'evy white
noises. The proposed numerical procedure relies on the introduction of an
integral transform of Wiener-Hopf type into the equation governing the
characteristic function. Once this equation is rewritten as partial
integro-differential equation, it is then solved by applying the method of
convolution quadrature originally proposed by Lubich, here extended to deal
with this particular integral transform. The proposed approach is relevant for
two reasons: 1) Statistics of systems with several different drift terms can be
handled in an efficient way, independently from the kind of white noise; 2) The
particular form of Wiener-Hopf integral transform and its numerical evaluation,
both introduced in this study, are generalizations of fractional
integro-differential operators of potential type and Gr\"unwald-Letnikov
fractional derivatives, respectively.Comment: 20 pages, 5 figure
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
On the well-posedness of the stochastic Allen-Cahn equation in two dimensions
White noise-driven nonlinear stochastic partial differential equations
(SPDEs) of parabolic type are frequently used to model physical and biological
systems in space dimensions d = 1,2,3. Whereas existence and uniqueness of weak
solutions to these equations are well established in one dimension, the
situation is different for d \geq 2. Despite their popularity in the applied
sciences, higher dimensional versions of these SPDE models are generally
assumed to be ill-posed by the mathematics community. We study this discrepancy
on the specific example of the two dimensional Allen-Cahn equation driven by
additive white noise. Since it is unclear how to define the notion of a weak
solution to this equation, we regularize the noise and introduce a family of
approximations. Based on heuristic arguments and numerical experiments, we
conjecture that these approximations exhibit divergent behavior in the
continuum limit. The results strongly suggest that a series of published
numerical studies are problematic: shrinking the mesh size in these simulations
does not lead to the recovery of a physically meaningful limit.Comment: 21 pages, 4 figures; accepted by Journal of Computational Physics
(Dec 2011
Variational Perturbation Theory for Fokker-Planck Equation with Nonlinear Drift
We develop a recursive method for perturbative solutions of the Fokker-Planck
equation with nonlinear drift. The series expansion of the time-dependent
probability density in terms of powers of the coupling constant is obtained by
solving a set of first-order linear ordinary differential equations. Resumming
the series in the spirit of variational perturbation theory we are able to
determine the probability density for all values of the coupling constant.
Comparison with numerical results shows exponential convergence with increasing
order.Comment: Author Information under
http://www.theo-phys.uni-essen.de/tp/ags/pelster_dir
A constructive mean field analysis of multi population neural networks with random synaptic weights and stochastic inputs
We deal with the problem of bridging the gap between two scales in neuronal
modeling. At the first (microscopic) scale, neurons are considered individually
and their behavior described by stochastic differential equations that govern
the time variations of their membrane potentials. They are coupled by synaptic
connections acting on their resulting activity, a nonlinear function of their
membrane potential. At the second (mesoscopic) scale, interacting populations
of neurons are described individually by similar equations. The equations
describing the dynamical and the stationary mean field behaviors are considered
as functional equations on a set of stochastic processes. Using this new point
of view allows us to prove that these equations are well-posed on any finite
time interval and to provide a constructive method for effectively computing
their unique solution. This method is proved to converge to the unique solution
and we characterize its complexity and convergence rate. We also provide
partial results for the stationary problem on infinite time intervals. These
results shed some new light on such neural mass models as the one of Jansen and
Rit \cite{jansen-rit:95}: their dynamics appears as a coarse approximation of
the much richer dynamics that emerges from our analysis. Our numerical
experiments confirm that the framework we propose and the numerical methods we
derive from it provide a new and powerful tool for the exploration of neural
behaviors at different scales.Comment: 55 pages, 4 figures, to appear in "Frontiers in Neuroscience
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