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Wednesday, May 6, 2026 - 16:15 in Zoom


Global well-posedness for the two-dimensional dispersive Anderson model and its large torus limit

A talk in the Oberseminar Analysis series by
Ruoyuan Liu from Bonn

Abstract: In this talk, we consider the two-dimensional nonlinear Schrödinger equation with a multiplicative spatial white noise and a polynomial nonlinearity, also known as the dispersive Anderson model (DAM). The talk is split into two parts. In the first part, we study global well-posedness for the DAM on the plane. We proceed by using a gauge transform introduced by Hairer and Labbé (2015) on the parabolic Anderson model and constructing the solution as a limit of solutions to a family of approximating equations. To establish global well-posedness, we establish a priori bounds using the Hamiltonian structure of the equation and also Strichartz estimates. In order to control the logarithmic growth of the noise, we incorporate function spaces with polynomial weights in our analysis. This part is based on a joint work with Arnaud Debussche (ENS Rennes), Nikolay Tzvetkov (ENS Lyon), and Nicola Visciglia (University of Pisa). In the second part, we show that the global solution of the DAM on the plane can be realized as a limit of the periodic global dynamics of the DAM as the period goes to infinity, given suitable initial conditions and periodization of the noise. Global well-posedness of the periodic DAM was shown by Tzvetkov and Visciglia (2023), but the global solutions are not uniform in periods due to the logarithmic growth of the noise. To overcome this issue, we introduce periodic weights and construct weighted function spaces on periodic domains, which allow us to obtain a priori bounds for solutions independent of the periodicity. This part is based on a joint work with Nikolay Tzvetkov (ENS Lyon).

Zoom-Meeting beitreten uni-bielefeld.zoom-x.de/j/64951709096?pwd=EfJynCkou8bBw77CsNYMuh88IrKazJ.1

Meeting-ID: 649 5170 9096 Passwort: 351532

Within the CRC this talk is associated to the project(s): A1



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