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Modelling Nonlinear Dynamical Systems

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Systems that evolve over time are ubiquitous to everyday life: from cars and planes to communications to the weather to chemical and biological processes. Building reliable models of these systems is a prerequisite to understanding them and for prediction and control. However, the more interesting dynamical systems are typically nonlinear and can only be observed indirectly through noisy measurements. In this RCC we will give an overview of some of the proposed methods for modeling nonlinear dynamical systems; we will cover both analytical solutions and those based on advanced sampling methods.

This talk is part of the Machine Learning Reading Group @ CUED series.

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