Pages that link to "Item:Q1648111"
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The following pages link to Automated tuning for parameter identification and uncertainty quantification in multi-scale coronary simulations (Q1648111):
Displaying 15 items.
- Uncertainty quantification of simulated biomechanical stimuli in coronary artery bypass grafts (Q1986799) (← links)
- One-dimensional modeling of fractional flow reserve in coronary artery disease: uncertainty quantification and Bayesian optimization (Q1988087) (← links)
- Semi-automatic method of stent development for hemodynamic simulations in patient coronary arteries with disease (Q2080413) (← links)
- A reduced unified continuum formulation for vascular fluid-structure interaction (Q2136709) (← links)
- Perivascular pumping in the mouse brain: improved boundary conditions reconcile theory, simulation, and experiment (Q2137455) (← links)
- Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics (Q2184337) (← links)
- Hemodynamics and stresses in numerical simulations of the thoracic aorta: stochastic sensitivity analysis to inlet flow-rate waveform (Q2245543) (← links)
- Geometric uncertainty in patient-specific cardiovascular modeling with convolutional dropout networks (Q2246251) (← links)
- A review on the biomechanics of coronary arteries (Q2295779) (← links)
- Performance of preconditioned iterative linear solvers for cardiovascular simulations in rigid and deformable vessels (Q2322962) (← links)
- Computational modeling of cardiac hemodynamics: current status and future outlook (Q2374981) (← links)
- Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems (Q2679283) (← links)
- Branched latent neural maps (Q6118560) (← links)
- Uncertainty quantification in parameter estimation using physics-integrated machine learning (Q6610958) (← links)
- An augmented streamline upwind/Petrov-Galerkin method for the time-spectral convection-diffusion equation (Q6648391) (← links)
