Data-driven Control of Complex Systems<br/>

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Data-driven Control of Complex Systems

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 54920 (04)
University: Sharif University of Technology
Department: Physics
Advisor(s): Rahimi Tabar, Mohammad Reza
Abstract:
In this thesis, we first briefly review the basic concepts of stochastic processes. After reviewing and studying the dynamic equation that can explain a stochastic process, we show how one can find on a data-driven basis, the first-, second- and higher-order interactions between different subunits of a complex system by disentangling the dynamics of multivariate time series into stochastic and deterministic parts. Our data-driven approach is to detect different degrees of interactions obtained using conditional moments of Kramers-Moyal coefficients from unconditioned correlation functions and statistical moments of multivariate N-dimensional multivariate time series. Finally, we study the controllability of these complex networks, and by constructing the N−dimensional time-series interaction matrix near the fixed points, we find the driving nodes. Controlling a network means controlling that complex net- work from an initial state to an arbitrary final state in a finite amount of time
Keywords:
Stochastic Process ; Dynamical Systems ; Complex Systems Control ; Higher-Order Kuramoto Oscillators ; Data Driven Method