- Kinetic FRG
Selected topics in transport phenomena: deterministic and probabilistic aspects
Apr 18 - 21, 2017
Ctr for Scientific Computation & Math. Modeling, UMd
The interplay between deterministic and stochastic modeling of transport phenomena is a broad subject with an increasing interest fueled by applications which arise in physical, biological and social systems. Among recent analytical developments we mention development of new tools from micro-local to non-local in deterministic transport and their interplay in UQ, random media and averaging phenomena in stochastic transport. These analytical developments, complemented by numerical simulations of transport processes are found in modern applications, from kinetic theory and collective dynamics to financial math, ``games" and fluid mechanics. These will be the focus of this workshop.
This workshop will bring together researchers with diverse expertise on deterministic and stochastic methods for transport processes. Our goal is to stimulate interdisciplinary discussion between mathematicians and statistical and biological physicists. We will particularly focus on similarities and differences arising in different fields using stochastic and deterministic modeling. The aim is to identify common theoretical and computational challenges and discuss suitable techniques that can be successfully applied in different application fields.
Invited participants can RSVP and provide the additional requested information about their stay, including the title and abstract of proposed talks, here.
Applications for participation can be made through the Online Application. Due to space limitations, these requests are subject to approval by the organizers.
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
Ctr for Scientific Computation & Math. Modeling, UMd (CSCAMM,UMd)
Funding provided by the NSF through the KI-net Grant.