<ul><li>This work compares different parametric Dynamic Mode Decomposition (DMD) algorithms for thermal-hydraulics applications.</li><li>DMD is an equation-free technique used to learn linear models from time series datasets.</li><li>The standard DMD formulation cannot handle parametric time series, requiring different linear models for each parameter realization.</li><li>The study explores three different thermal-hydraulics problems to assess the advantages and shortcomings of the deployed algorithms.</li></ul>

A Comparison of Parametric Dynamic Mode Decomposition Algorithms for Thermal-Hydraulics Applications

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