For more information about this meeting, contact Hope Shaffer, Chun Liu.
|Title:||Extracting and predicting spatiotemporal patterns from data with dynamics-adapted kernels|
|Seminar:||Computational and Applied Mathematics Colloquium|
|Speaker:||Dimitrios Giannakis, New York University (Host: J Harlim)|
|Kernel methods provide an attractive way of extracting features from data by biasing their geometry in a controlled manner. In this talk, we discuss a family of kernels for dynamical systems featuring an explicit dependence on the dynamical vector field operating in the phase-space manifold, estimated empirically through finite differences of time-ordered data samples. The associated diffusion operator for data analysis is adapted to the dynamics in that it generates diffusions along the integral curves of the dynamical vector field. We present applications to toy dynamical systems and comprehensive climate models. We also discuss a technique for analog forecasting based on these kernels. In this empirical forecasting technique, kernels are used to create weighted ensembles of states (analogs) with high similarity to the initial data from a record of historical observations, and the future values of observables are predicted from the historical evolution of the ensemble.|
Room Reservation Information
|Date:||10 / 06 / 2014|
|Time:||02:30pm - 03:30pm|