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- W4287183793 abstract "Algebraic or geometric multigrid methods are commonly used in numerical solvers as they are a multi-resolution method able to handle problems with multiple scales. In this work, we propose a modification to the commonly-used U-Net neural network architecture that is inspired by the principles of multigrid methods, referred to here as U-Net-MG. We then demonstrate that this proposed U-Net-MG architecture can successfully reduce the test prediction errors relative to the conventional U-Net architecture when modeling a set of fluid dynamic problems. In total, we demonstrate an improvement in the prediction of velocity and pressure fields for the canonical fluid dynamics cases of flow past a stationary cylinder, flow past 2 cylinders in out-of-phase motion, and flow past an oscillating airfoil in both the propulsion and energy harvesting modes. In general, while both the U-Net and U-Net-MG models can model the systems well with test RMSEs of less than 1%, the use of the U-Net-MG architecture can further reduce RMSEs by between 20% and 70%." @default.
- W4287183793 created "2022-07-25" @default.
- W4287183793 creator A5012761705 @default.
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- W4287183793 date "2021-05-09" @default.
- W4287183793 modified "2023-09-29" @default.
- W4287183793 title "Surrogate Modeling of Fluid Dynamics with a Multigrid Inspired Neural Network Architecture" @default.
- W4287183793 doi "https://doi.org/10.48550/arxiv.2105.03854" @default.
- W4287183793 hasPublicationYear "2021" @default.
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