{"id":475,"date":"2020-03-27T23:51:29","date_gmt":"2020-03-28T03:51:29","guid":{"rendered":"http:\/\/sites.nd.edu\/jianxun-wang\/?p=475"},"modified":"2020-03-27T23:51:29","modified_gmt":"2020-03-28T03:51:29","slug":"new-publication-physics-constrained-bayesian-neural-network-for-fluid-flow-reconstruction-with-sparse-and-noisy-data-in-taml","status":"publish","type":"post","link":"https:\/\/sites.nd.edu\/jianxun-wang\/2020\/03\/27\/new-publication-physics-constrained-bayesian-neural-network-for-fluid-flow-reconstruction-with-sparse-and-noisy-data-in-taml\/","title":{"rendered":"New publication: Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data in TAML"},"content":{"rendered":"<p>L. Sun*, J.-X. Wang, Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data, <em>Theoretical and Applied Mechanics Letters<\/em>, (Accepted), 2020 [<a href=\"https:\/\/arxiv.org\/abs\/2001.05542\">Arxiv<\/a>, DOI, bib]<\/p>\n<p>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.<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>L. Sun*, J.-X. Wang, Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data, Theoretical and Applied Mechanics Letters, (Accepted), 2020 [Arxiv, DOI, bib] 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. [&hellip;]<\/p>\n","protected":false},"author":3220,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[28527],"tags":[],"class_list":["post-475","post","type-post","status-publish","format-standard","hentry","category-publication"],"_links":{"self":[{"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/posts\/475","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/users\/3220"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/comments?post=475"}],"version-history":[{"count":1,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/posts\/475\/revisions"}],"predecessor-version":[{"id":476,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/posts\/475\/revisions\/476"}],"wp:attachment":[{"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/media?parent=475"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/categories?post=475"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.nd.edu\/jianxun-wang\/wp-json\/wp\/v2\/tags?post=475"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}