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• W3000676377 abstract "In many applications, flow measurements are usually sparse and possibly noisy. The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging. In this work, we propose an innovative physics-constrained Bayesian deep learning approach to reconstruct flow fields from sparse, noisy velocity data, where equation-based constraints are imposed through the likelihood function and uncertainty of the reconstructed flow can be estimated. Specifically, a Bayesian deep neural network is trained on sparse measurement data to capture the flow field. In the meantime, the violation of physical laws will be penalized on a large number of spatiotemporal points where measurements are not available. A non-parametric variational inference approach is applied to enable efficient physics-constrained Bayesian learning. Several test cases on idealized vascular flows with synthetic measurement data are studied to demonstrate the merit of the proposed method." @default.
• W3000676377 created "2020-01-23" @default.
• W3000676377 creator A5085043351 @default.
• W3000676377 creator A5087758954 @default.
• W3000676377 date "2020-01-15" @default.
• W3000676377 modified "2023-10-17" @default.
• W3000676377 title "Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data" @default.
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• W3000676377 doi "https://doi.org/10.48550/arxiv.2001.05542" @default.
• W3000676377 hasPublicationYear "2020" @default.
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