{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:14:52Z","timestamp":1740100492953,"version":"3.37.3"},"reference-count":27,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T00:00:00Z","timestamp":1639440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T00:00:00Z","timestamp":1639440000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T00:00:00Z","timestamp":1639440000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,14]]},"DOI":"10.1109\/cdc45484.2021.9683353","type":"proceedings-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T20:50:18Z","timestamp":1643748618000},"page":"2911-2916","source":"Crossref","is-referenced-by-count":1,"title":["Distributed Bayesian Parameter Inference for Physics-Informed Neural Networks"],"prefix":"10.1109","author":[{"given":"He","family":"Bai","sequence":"first","affiliation":[]},{"given":"Kinjal","family":"Bhar","sequence":"additional","affiliation":[]},{"given":"Jemin","family":"George","sequence":"additional","affiliation":[]},{"given":"Carl","family":"Busart","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1137\/18M1229845"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1137\/18M1225409"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1137\/19M1260141"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1364\/OE.384875"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaw4741"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2020.113250"},{"key":"ref16","first-page":"q02.004","article-title":"Physics-Informed Neural Networks for the Modelling of Fluid-Structure Interactions","author":"ang","year":"2020","journal-title":"APS Division of Fluid Dynamics Meeting Abstracts"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.116641"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2019.07.048"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2020.109913"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1137\/17M1120762"},{"key":"ref27","article-title":"Decentralized Langevin dynamics for Bayesian learning","volume":"33","author":"parayil","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2017.07.050"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1214\/15-EJS989"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2017.11.039"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2020.113552"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2019.112789"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2019.109020"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2017.01.060"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-0745-0"},{"key":"ref22","article-title":"Practical variational inference for neural networks","volume":"24","author":"graves","year":"2011","journal-title":"Advances in neural information processing systems"},{"article-title":"MCMC using Hamiltonian dynamics","year":"2012","author":"neal","key":"ref21"},{"key":"ref24","first-page":"186","article-title":"Convergence of Langevin MCMC in KL-divergence","author":"cheng","year":"2018","journal-title":"Machine Learning Research"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1820003116"},{"key":"ref26","first-page":"1674","article-title":"Non-convex learning via stochastic gradient Langevin dynamics: a nonasymptotic analysis","volume":"65","author":"raginsky","year":"2017","journal-title":"Proceedings of the 2017 Conference on Learning Theory"},{"key":"ref25","first-page":"8094","article-title":"Rapid convergence of the unadjusted Langevin algorithm: Isoperimetry suffices","author":"vempala","year":"2019","journal-title":"Advances in neural information processing systems"}],"event":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","start":{"date-parts":[[2021,12,14]]},"location":"Austin, TX, USA","end":{"date-parts":[[2021,12,17]]}},"container-title":["2021 60th IEEE Conference on Decision and Control (CDC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9682670\/9682776\/09683353.pdf?arnumber=9683353","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T16:58:02Z","timestamp":1652201882000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9683353\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,14]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1109\/cdc45484.2021.9683353","relation":{},"subject":[],"published":{"date-parts":[[2021,12,14]]}}}