Automated tuning for parameter identification and uncertainty quantification in multi-scale coronary simulations

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• 作者：Justin S. Tran^a ; Daniele E. Schiavazzi^a ; Abhay B. Ramachandra^a ; Andrew M. Kahn^b ; Alison L. Marsden ; ^a ; ^{amarsden@stanford.edu}
• 关键词：Coronary flow ; Hemodynamics ; Multiscale cardiovascular simulation ; Data assimilation ; Parameter estimation ; Lumped boundary circulation models ; Uncertainty quantification
• 刊名：Computers & Fluids
• 出版年：2017
• 出版时间：5 January 2017
• 年：2017
• 卷：142
• 期：Complete
• 页码：128-138
• 全文大小：2907 K
• 卷排序：142

文摘

A framework is presented for automated tuning of lumped parameter network boundary conditions in cardiovascular simulations. An automated Bayesian approach is employed, achieving excellent agreement with clinical targets. The method is demonstrated using patient specific data for six coronary bypass patients and one normal. Uncertainties are propagated to 3D simulation results to demonstrate success of the method in a multi-scale framework.