Abstract
This article illustrates a review on the applications of a new method that can be used either as a reduced order model or to uncover the underlying physics in spatio-temporal data. The method is based on the higher order dynamic mode decomposition (a recent extension of standard dynamic mode decomposition) of the given data, which leads to a purely data-driven, equation free approach. The high accuracy and robustness of the method makes it suitable to analyze very complex spatio-temporal data resulting from either numerical simulations or experimental measurements. The article illustrates the good performance and versatility of this new reduced order model in two specific applications: (i) speeding up numerical simulations in the wake of a circular cylinder and (ii) wind forecasting upstream wind turbines using actual experimental data databases.
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