Forward and Inverse Problems in Kinetic TheoryOct 25 - 27, 2019University of Wisconsin-Madison |
ABSTRACTComplex particle systems can be modeled, at mesoscopic scale using statistical mechanics language, by kinetic type equations that characterize particle interactions. It closely connects macroscopic diffusion laws, classical or fractional, and microscopic particle interactions via mean field theories. A classical fundamental kinetic model is the Boltzmann type equations, and in different regimes they have been extensively used to describe physical phenomena emerging in rarefied gas theories, plasma interactions, charge transport in solid such as semiconductor, as much as energy transfer and reactive interface problems. At the same time, substantial progress has been achieved in investigating and understanding Inverse Problems for reconstruction of images, signals and sharp interface recognition in a random or periodic media. The techniques accumulated in this area of studying may need to be modified to incorporate the time space transport scales in kinetic theory, enticing the development of connections between the kinetic transport models and inverse problem studies. This workshop aims to bring experts in the areas of inverse problems and kinetic transport theory to exchange ideas as much as to initiate potential collaborations. GOALSThis workshop aims to bring together researchers with different expertise in kinetic theory and inverse problems. Our goal is to assess the current state-of-the-arts inverse techniques and discuss their potential applications in vast kinetic type equations. REGISTRATION CLOSEDNEW APPLICANTS. Due to the large number of applications, we regret that RSVP is now closed to new applicants. |
CONFIRMED PARTICIPANTSFUNDINGA 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 PARTICIPANTSDepartment of Mathematics480 Lincoln Dr. University of Wisconsin-Madison Madison, WI Email: qinli@math.wisc.edu ACKNOWLEDGMENTFunding provided by the NSF through the KI-net Grant. |