Manifold Learning for Data driven Dynamical System Modeling скачать в хорошем качестве

Manifold Learning for Data driven Dynamical System Modeling

Manifold Learning for Data driven Dynamical System Modeling at the Technion - Israel Institute of Technology (an ICASSP 2019 Demo) The extraction of models from data (in a sense, the “understanding” of the physical laws giving rise to the data) is a fundamental cognitive as well as scientific challenge. The demonstration we present revolves around a geometric/analytic learning approach capable of creating minimal descriptions of parametrically-dependent unknown nonlinear dynamical systems. This is accomplished by the data-driven discovery of useful intrinsic-state variables and parameters in terms of which one can empirically model the underlying dynamics. This approach follows recent trends in data analysis and signal processing, operating directly on observations, systematically creating accurate representations from data, without deriving models in closed-form and without any prior knowledge about the system dynamics. In particular, we present a kernel-based manifold learning approach, which learns the intrinsic geometric structure underlying the observations by capturing and exploiting the co-dependencies between the different dimensions of the data. Based on the paper: O. Yair, R. Talmon, R. R. Coifman, I. G. Kevrekidis, Reconstruction of normal forms by learning informed observation geometries from data, Proceedings of the National Academy of Sciences (PNAS), 201620045, 2017. Undergraduate students: Kobi Shiran, Gal Kinberg Supervisor: Or Yair Signal and Image Processing Laboratory (SIPL) Andrew and Erna Viterby Faculty of Electrical Engineering Music by: http://www.purple-planet.com/ Follow these links to learn more: https://sipl.eelabs.technion.ac.il/ https://ronentalmon.com/