Polynomial chaos enhanced by dynamic mode decomposition for order-reduction of dynamic models

Thanks to their low computational cost, reduced-order models (ROMs) are indispensable in ensemble-based simulations used, e.g., for uncertainty quantification, inverse modeling, and optimization. Since data used to train a ROM are typically obtained by running a high-fidelity model (HFM) multiple ti...

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Veröffentlicht in:	Advances in water resources 2024-04, Vol.186 (C), p.104677, Article 104677
Hauptverfasser:	Libero, G., Tartakovsky, D.M., Ciriello, V.
Format:	Artikel
Sprache:	eng
Schlagworte:	Dynamic mode decomposition Dynamic model Hydrology Polynomial chaos expansion Reduced-order model uncertainty water
Online-Zugang:	Volltext
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