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

Date: August 14
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.

Towards optimal control of gliders for velocity field assimilation
Moore, Richard (New Jersey Inst. of Tech.)

Uncertainty quantification (and sensitivity!) in fluid dynamics and control
Brunton, Steven (Univ. of Washington)

Statistical Learning for model reduction with ATLAS
Maggioni, Mauro (Duke Univ.)
Crosskey, Miles (Duke Univ.)
Weare, Jonathan (Univ. of Chicago)

Timescale separation and forecasting with dynamics-adapted kernels
Giannakis, Dimitrios (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