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Data Driven Methods for Complex Turbulent Systems ( 3 ) - Andrew J. Majda

1,729 views · Published 19 April 2013 · 1:31:17 · Indexed 21 October 2026

Channel: International Centre for Theoretical Sciences · 2013 · Science & Technology

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Lecture 3: Data Driven Methods for Complex Turbulent Systems

Abstract:
An important contemporary research topic is the development of physics constrained data driven methods for complex, large-dimensional turbulent systems such as the equations for climate change science. Three new approaches to various aspects of this topic are emphasized here: 1) the systematic development of physics constrained quadratic regression models with memory for low frequency components of complex systems; 2) Novel dynamic stochastic superresolution algorithms for real time filtering of turbulent systems; 3) New nonlinear Laplacian Spectral Analysis (NLSA) algorithms for large dimensional time series which capture both intermittency and low frequency variability unlike conventional EOF or principal component analysis. This is joint work with John Harlim (1, 2), Michal Branicki (2), and Dimitri Giannakis (3). Examples will include dynamic stochastic superresolution (DSS) for the mesoscale eddy heat flux from coarse satellite altimetry measurements and low frequency and intermittent modes in SST form 800 year runs of CCM. All of the references in this lecture can be found at http://www.math.nyu.edu/faculty/majda/.

Date: January 24, 2013
Time: 4:00pm - 5:30pm
Vanue: Seminar Hall, ICTS-TIFR, IISc
(Participation by Invitation only)

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