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Hybrid neural network-finite element river flow model
Results obtained from a hybrid neural network-finite element model are reported in this paper. The hybrid model incorporates artificial neural network (ANN) nodes into a numerical scheme, which solves the two-dimensional shallow water equations using finite elements (FE). First, numerical computations are carried out on the entire numerical model, using a larger mesh. The results from this computation are then used to train several preselected ANN nodes. The ANN nodes model the response for a part of the entire numerical model by transferring the system reaction to the location where both models are connected in real time. This allows a smaller mesh to be used in the hybrid ANN-FE model, resulting in savings in computation time. The hybrid model was developed for a river application, using the computational nodes located at the open boundaries to be the ANN nodes for the ANN-FE hybrid model. Real-time coupling between the ANN and FE models was achieved, and a reduction is CPU time of more than 25% was obtained.
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Journal
Journal of hydraulic engineeringVolume
131Issue
1Pagination
52 - 59Publisher
American Society of Civil EngineersLocation
Reston, Va.ISSN
0733-9429Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2005, ASCEUsage metrics
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