Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Multicompartment Models: Overview

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

MRI-Based Pressure Gradient Mapping in Patient-Specific Models of Coarctation of the Aorta.

medRxiv : the preprint server for health sciences·2026

Same author

Impact of guideline definitions on right ventricular diameter in echocardiography: an automated analysis in controls and patients with pulmonary hypertension.

Echo research and practice·2026

Same author

SDFStent: Real-time interactive virtual stenting via SDF deformation fields.

ArXiv·2026

Same author

Per-vessel myocardial blood flow improvement after coronary artery bypass graft surgery quantified by CT myocardial perfusion imaging.

Journal of cardiovascular computed tomography·2026

Same author

A Continuum of Atrial Peristalsis Initiates the Bicuspid to Quadricuspid Valve Transition.

bioRxiv : the preprint server for biology·2026

Same author

Simulations predict improved valve performance without direct leaflet intervention after neonatal truncus arteriosus repair.

The Journal of thoracic and cardiovascular surgery·2026

Search research articles

Related Experiment Video

Updated: Dec 23, 2025

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics.

Casey M Fleeter¹, Gianluca Geraci², Daniele E Schiavazzi³

¹Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.

Computer Methods in Applied Mechanics and Engineering

|April 28, 2020

Summary

This summary is machine-generated.

This study introduces an efficient uncertainty quantification framework for cardiovascular modeling using multilevel multifidelity Monte Carlo (MLMF) methods. The MLMF approach significantly reduces computational cost (10-100x) for hemodynamic analysis in cardiovascular simulations.

Keywords:

Cardiovascular modeling Multifidelity Monte Carlo Multilevel Monte Carlo Multilevel multifidelity Monte Carlo Uncertainty quantification

More Related Videos

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Published on: September 1, 2014

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

Published on: December 6, 2024

Related Experiment Videos

Last Updated: Dec 23, 2025

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Published on: September 1, 2014

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

Published on: December 6, 2024

Area of Science:

Computational Fluid Dynamics
Biomedical Engineering
Uncertainty Quantification

Background:

Standard uncertainty quantification in cardiovascular modeling is computationally expensive.
High-fidelity 3D simulations require significant resources, limiting analysis.
Variability in hemodynamic outputs is crucial for understanding cardiovascular health and disease.

Purpose of the Study:

To develop an efficient uncertainty quantification framework for cardiovascular models.
To reduce the computational cost of estimating hemodynamic quantities of interest.
To compare the efficiency of different MLMF estimators.

Main Methods:

Utilized a multilevel multifidelity Monte Carlo (MLMF) estimator.
Employed zero- and one-dimensional low-fidelity models alongside high-fidelity 3D models.
Leveraged varying spatial resolutions for cardiovascular models.

Main Results:

Achieved a 10 to 100 times reduction in computational cost using MLMF estimators.
Demonstrated greater efficiency for global hemodynamic quantities compared to local ones.
Observed larger computational cost reductions in healthy models versus diseased models.

Conclusions:

The proposed MLMF framework makes uncertainty quantification feasible within constrained computational budgets.
MLMF estimators offer significant computational savings for cardiovascular modeling.
The framework effectively quantifies variability in hemodynamic outputs for both healthy and diseased states.