PSU Mark
Eberly College of Science Mathematics Department

Meeting Details

For more information about this meeting, contact Stephanie Zerby, Chun Liu, Amy Hanley.

Title:Data-driven stochastic model reduction in nonlinear dynamical systems
Seminar:Department of Mathematics Colloquium
Speaker:Fei Lu, University of California, Berkeley
Prediction of high-dimensional chaotic dynamic systems is often difficult when only partial observations are available, because such systems are often expensive to solve in full and the initial data will be incomplete. The development of reduced models for the observed variables is thus needed. The challenges come from the nonlinear interactions between the observed variables and the unobserved variables, and the difficulties in quantifying uncertainties from discrete data. We address these challenges by developing discrete-time stochastic reduced systems for the observable variables, by using data and statistical methods to account for the impact of the unobserved variables. A key ingredient in the construction of the stochastic reduced systems is a discrete-time stochastic parametrization based on inference of nonlinear time series. We demonstrate our approach on the two-layer Lorenz 96 system and the Kuramoto-Sivashinsky equation. A theoretical understanding of such a data-driven modeling problem requires ideas from dynamical systems, PDEs, probability and statistics. Open questions will be discussed.

Room Reservation Information

Room Number:MB114
Date:02 / 25 / 2016
Time:03:35pm - 04:35pm