Parametric POD-Galerkin reduced-order models for turbulent flows are augmented with deep-operator-network closure terms that reintroduce discarded-mode contributions, improving pressure and velocity accuracy over the baseline.
Proper orthogonal decomposition closure models for fluid flows: Burgers equation
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abstract
This paper puts forth several closure models for the proper orthogonal decomposition (POD) reduced order modeling of fluid flows. These new closure models, together with other standard closure models, are investigated in the numerical simulation of the Burgers equation. This simplified setting represents just the first step in the investigation of the new closure models. It allows a thorough assessment of the performance of the new models, including a parameter sensitivity study. Two challenging test problems displaying moving shock waves are chosen in the numerical investigation. The closure models and a standard Galerkin POD reduced order model are benchmarked against the fine resolution numerical simulation. Both numerical accuracy and computational efficiency are used to assess the performance of the models.
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Data-driven Closure Strategies for Parametrized Reduced Order Models via Deep Operator Networks
Parametric POD-Galerkin reduced-order models for turbulent flows are augmented with deep-operator-network closure terms that reintroduce discarded-mode contributions, improving pressure and velocity accuracy over the baseline.