Automated tuning for parameter identification and uncertainty quantification in multi-scale coronary simulations
DOI10.1016/j.compfluid.2016.05.015zbMath1390.76945OpenAlexW2397934715WikidataQ38978043 ScholiaQ38978043MaRDI QIDQ1648111
Alison L. Marsden, Andrew M. Kahn, Daniele E. Schiavazzi, Abhay B. Ramachandra, Justin S. Tran
Publication date: 27 June 2018
Published in: Computers and Fluids (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.compfluid.2016.05.015
parameter estimationhemodynamicsdata assimilationuncertainty quantificationcoronary flowlumped boundary circulation modelsmultiscale cardiovascular simulation
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
Related Items (11)
Cites Work
- Unnamed Item
- Adaptive proposal distribution for random walk Metropolis algorithm
- Space-time and ALE-VMS techniques for patient-specific cardiovascular fluid-structure interaction modeling
- A fully-coupled fluid-structure interaction simulation of cerebral aneurysms
- Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
- A generalized-\(\alpha\) method for integrating the filtered Navier-Stokes equations with a stabilized finite element method
- Inference from iterative simulation using multiple sequences
- Finite element modeling of blood in arteries
- Impact of geometric uncertainty on hemodynamic simulations using machine learning
- Identification in Parametric Models
This page was built for publication: Automated tuning for parameter identification and uncertainty quantification in multi-scale coronary simulations