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A discrete-time approach to data-based stochastic model reduction for chaotic systems

PDE and Applied Math Seminar

Speaker: Fei Lu, LBNL
Location: 2112 MSB
Start time: Thu, Nov 10 2016, 4:10PM

The need to deduce reduced computational models from discrete observations of
complex systems arises in many areas of science and engineering, e.g. in 
meteorology, climate modeling, and materials science. The challenges come 
mainly from non-Markovian effects and nonlinear interactions between 
observed and unobserved variables, and from the difficulty in inference from 
discrete data.

We address these challenges by developing discrete-time stochastic models. We 
discuss the comparative advantages of discrete models and show by example that 
they can capture the long-time statistics and can be used to make medium-term 
predictions. The examples include the Lorenz 96 model (which is a simplified 
model of the atmosphere) and the Kuramoto-Sivashinski model of 
spatiotemporally chaotic dynamics.

Please join us after the talk at Bistro 33 for free hors d’oeuvres. All are welcome.