[2] Keynote 2

[2-1] February 27, 11:05-11:50


Principled Data Assimilation in Nonlinear Complex Systems

Henry Abarbanel (UC San Diego)


We formulate a framework for transferring information from an observed complex nonlinear system to a physically based model of the processes underlying the observations. To evaluate the statistical path integral involved, we discuss in some detail the variational method of Laplace for evaluating the required integrals. We discuss how to determine how many observations are required to allow for accurate state estimations and predictions. We discuss how to proceed when too few measurements are available. We introduce a method to find the global minimum of the nonlinear function, the action, identified in the path integral. Examples are drawn from neurobiology and from numerical weather prediction.

  Presentation file: 02_1_H.Abarbanel.pdf