DMS Stochastic Analysis Seminar

Time: Mar 29, 2023 (01:10 PM)
Location: 352 Parker Hall



Speaker: Yu Gu, University of Maryland

Title: KPZ on a large torus

Abstract: I will present the recent work with Tomasz Komorowski and Alex Dunlap in which we derived optimal variance bounds on the solution to the KPZ equation on a large torus, in certain regimes where the size of the torus increases with time. We mostly use the tools from stochastic calculus and I will also try to give a heuristic explanation of the 2/3 and 1/3 exponents in the 1+1 KPZ universality class.


Short bio: Dr. Yu Gu, Associate Professor of Mathematics at the University of Maryland, College Park, obtained his bachelor's degree in mathematics from Tsinghua University in 2009 and his Ph.D. in mathematics from Columbia University in 2014. After completing his Ph.D., Dr. Gu worked as a postdoctoral
researcher at Stanford University until 2017. Then he joined Carnegie Mellon University as an Assistant Professor until 2021 before moving to his current position at the University of Maryland.
Dr. Gu's research interests include random dynamics, statistical mechanics, stochastic homogenization, and stochastic partial differential equations (SPDEs). His recent work has focused on studying the Kardar-Parisi-Zhang (KPZ) equation on large torus, which has applications in statistical mechanics and quantum field theory. Dr. Gu has published his research in several leading mathematics and physics journals, including Annals of Probability, Probability Theory and Related Fields, Communications on Pure and Applied Mathematics, SIAM Journal on Mathematical Analysis, Transactions of the American Mathematical Society, Journal of Functional Analysis, and Nonlinearity.
Dr. Gu's research has been supported by several grants, including NSF DMS 2016 (Standard), NSF DMS 2018 (Continued), and NSF Career (Applied Math) 2021.