Self similar solutions in one-dimensional kinetic models: a probabilistic view

Mathematics – Probability

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

This paper deals with a class of Boltzmann equations on the real line, extensions of the well-known Kac caricature. A distinguishing feature of the corresponding equations is that the therein collision gain operators are defined by $N$-linear smoothing transformations. This kind of problems have been studied, from an essentially analytic viewpoint, in a recent paper by Bobylev, Gamba and Cercignani. Instead, the present work rests exclusively on probabilistic methods, based on techniques pertaining to the classical central limit problem and to the so-called fixed-point equations for probability distributions. An advantage of resorting to methods from the probability theory is that the same results - relative to self-similar solutions - as those obtained by Bobylev, Gamba and Cercignani, are here deduced under weaker conditions. In particular, it is shown how convergence to self--similar solution depends on the belonging of the initial datum to the domain of attraction of a specific stable distribution. Moreover, some results on the speed of convergence are given in terms of Kantorovich-Wasserstein and Zolotarev distances between probability measures.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Self similar solutions in one-dimensional kinetic models: a probabilistic view does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Self similar solutions in one-dimensional kinetic models: a probabilistic view, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Self similar solutions in one-dimensional kinetic models: a probabilistic view will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-499727

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.