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KI-Net Conference Announcement

Young Researchers Workshop: Stochastic and deterministic methods in kinetic theory

Nov 28 - Dec 2, 2016

Duke University
Department of Mathematics

Visitor Guide



CONFERENCE SCHEDULE



ABSTRACT

The kinetic theory naturally brings together deterministic and stochastic physical models, from the early days of modeling of gas molecules, to the mesoscopic description of collective behavior, and to the more recent uncertainty quantification in kinetic models. On the numerical aspects, while stochastic numerical methods like direct simulation Monte Carlo methods have been very popular, recent years also observe huge development in deterministic methods for kinetic theory and stochastic systems. This workshop will focus on the fascinating interplay between analysis of kinetic theory, deterministic and stochastic modeling, multiscale analysis and methods, with applications in physical, biological and social sciences.

GOALS

The goal of this conference is to bring together young, energetic researchers working in kinetic theory and related fields to exchange ideas and facilitate collaborations. The focus will include stochastic modeling in kinetic theory, multiscale modelling, quantum dynamics, and stochastic and deterministic numerical methods. 

REGISTRATION REQUESTED

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.

ORGANIZERS

NameAffiliationEmail
Jacob BedrossianUniversity of Maryland, CSCAMM and Department of Mathematicsjacob@cscamm.umd.edu
Alina ChertockNorth Carolina State University, Department of Mathematicschertock@math.ncsu.edu
Jianfeng LuDuke University, Department of Mathematicsjianfeng@math.duke.edu
Zhennan ZhouDuke University, Department of Mathematicszhennan@math.duke.edu

CONFIRMED PARTICIPANTS

NameAffiliation
Sona AkopianUniversity of Texas at Austin
Jacob BedrossianUniversity of Maryland
Zhenning CaiNational University of Singapore
Lihui ChaiUniversity of California, Santa Barbara
Ke ChenUniversity of Wisconsin-Madison
Alina ChertockNorth Carolina State University
Helge DietertParis 7 - Diderot
Chenjie FanMassachusetts Institute of Technology
Yuwei FanStanford University
Di FangUniversity of Wisconsin-Madison
Yuanyuan FengCarnegie Mellon University
Amic FrouvelleParis Dauphine University
Sophie HechtImperial College London
Jingwei HuPurdue University
Hengrui HuNorth Carolina State University
Mirjam Sarah KadelkaVirginia Tech
Moon-Jin KangUniversity of Texas at Austin
Lei LiDuke University
Jianfeng LuDuke University
Sean T. McQuadeRutgers-Camden
Christian B. MendlStanford University
Hana MizerovaUniversity of Mainz
Seyma N. OzcanNorth Carolina State University
Nastassia Pouradier DuteilRutgers University - Camden
Lee RicketsonNew York University
Scott SmithMax Planck Institute for Mathematics in the Sciences
Anna SzczekutowiczUniversity of Texas at Austin
Changhui TanRice University
Maja TaskovicUniversity of Pennsylvania and UT Austin
Chong WangGeorge Washington University
Zhenfu WangUniversity of Maryland
Alexander WatsonColumbia University
Karlan WolfkillNorth Carolina State University
Tong WuNorth Carolina State University
Xiaoqian XuCarnegie Mellon University
Bokai YanUniversity of California, Los Angeles
Yao YaoGeorgia Tech
Cheng YuUniversity of Texas at Austin
Yong ZhangNew York University, Courant Institute
Jia ZhaoUniversity of North Carolina at Chapel Hill
Zhennan ZhouDuke University


FUNDING

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

Visitor Guide

Department of Mathematics
Science Drive, Duke University, Durham, NC 27708
Duke University
Durham, NC

Email: jianfeng@math.duke.edu

CONFERENCE POSTER

ACKNOWLEDGMENT

Funding provided by the NSF through the KI-net Grant.