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- W2989195552 abstract "In this paper, we put forth an evolve-then-correct reduced order modeling approach that combines intrusive and nonintrusive models to take hidden physical processes into account. Specifically, we split the underlying dynamics into known and unknown components. In the known part, we first utilize an intrusive Galerkin method projected on a set of basis functions obtained by proper orthogonal decomposition. We then formulate a recurrent neural network emulator based on the assumption that observed data is a manifestation of all relevant processes. We further enhance our approach by using an orthonormality conforming basis interpolation approach on a Grassmannian manifold to address off-design conditions. The proposed framework is illustrated here with the application of two-dimensional co-rotating vortex simulations under modeling uncertainty. The results demonstrate highly accurate predictions underlining the effectiveness of the evolve-then-correct approach toward realtime simulations, where the full process model is not known a priori." @default.
- W2989195552 created "2019-11-22" @default.
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- W2989195552 date "2019-11-05" @default.
- W2989195552 modified "2023-09-23" @default.
- W2989195552 title "An evolve-then-correct reduced order model for hidden fluid dynamics" @default.
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- W2989195552 doi "https://doi.org/10.48550/arxiv.1911.02049" @default.
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