- Kinetic FRG
Collective Behavior: Macroscopic versus Kinetic Descriptions
May 19 - 23, 2014
Nonlinear nonlocal aggregation/diffusion equations are basic macroscopic models in many collective behaviour applications such as bacterial chemotaxis, swarming, and computational neuroscience, to name a few. Kinetic modelling is being derived in these applications to include a mesoscopic level of description bridging the microscopic to the macroscopic scales. This workshop will serve to foster the interaction between modellers and mathematicians interested in these applications.
In this conference we will also honor Eitan Tadmor's 60th birthday.
This workshop will focus on highlighting recent developments of mathematical analysis tools and methods, design of suitable numerical schemes, and numerical simulation in some selected new applications of the field of nonlinear nonlocal aggregation/diffusion and kinetic Partial Differential Equations. Among the numerous areas of applications, we will concentrate particularly on some examples which can be identified, at the modelling stage, as systems made out of a large number of "individuals" which show a "collective behaviour" and how to obtain from them "averaged" information. The behaviour of individuals can be typically modelled via stochastic/deterministic ODEs from which one obtains mesoscopic and/or macroscopic descriptions based on mean-field type PDEs leading to kinetic and/or continuum model systems. The interplay between the aggregation/interaction behaviour (nonlocal, nonlinear), the transport phenomena, and the diffusion, is the main goal of analysis of this workshop.
NEW APPLICANTS. Due to the large number of applications, we regret that RSVP is now closed to new applicants.
A limited amount of travel and local lodging is available for researchers in the early stages of their career who want to attend the full program, especially for graduate students and post-doctoral fellows.
INFORMATION FOR PARTICIPANTS
180 Queen gate
Room 144, Huxley Building
Funding provided by the NSF through the KI-net Grant.