Data-driven methods for quantifying uncertainty of multiscale dynamical systems - Part I of IV
For Part II, see MS-Th-E-06
For Part III, see MS-Fr-D-06
For Part IV, see MS-Fr-E-06

Date: August 13
Time: 13:30--15:30
Room: 201

(Note: Click title to show the abstract.)

Harlim, John (The Pennsylvania State Univ.)
Sapsis, Themistoklis (MIT)
Giannakis, Dimitrios (New York Univ.)

Abstract: A major challenge in contemporary applied science is to design efficient models for predicting dynamical behavior resulting from complex interaction of multiple scale processes. This task, implicitly, requires one to account for uncertainties of the models due to initial conditions, boundary conditions, model errors, and observation errors. A promising interdisciplinary approach to address such issue is with a data-driven statistical methods that combine ideas from dynamical systems theory, stochastic processes, statistics, and data analysis. This special session aims to bring together researchers from across the spectrum of disciplines related to data-driven methods to discuss the development and application of emerging ideas and techniques for these important and difficult practical issues.

Diffusion Forecast
Harlim, John (The Pennsylvania State Univ.)

Predicting the cloud patterns of the Madden-Julian Oscillation through a low-order nonlinear stochastic model
Chen, Nan (New York Univ.)
Giannakis, Dimitrios (New York Univ.)

Filtering with noisy Lagrangian tracers
Tong, Xin (Courant Inst. of Mathematical Sci.)
Chen, Nan (New York Univ.)


Code: Type-Date-Time-Room No.
Type : IL=Invited Lecture, SL=Special Lectures, MS=Minisymposia, IM=Industrial Minisymposia, CP=Contributed Papers, PP=Posters
Date: Mo=Monday, Tu=Tuesday, We=Wednesday, Th=Thursday, Fr=Friday
Time : A=8:30-9:30, B=10:00-11:00, C=11:10-12:10, BC=10:00-12:10, D=13:30-15:30, E=16:00-18:00, F=19:00-20:00, G=12:10-13:30, H=15:30-16:00
Room No.: TBA