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

The Uncertainty Principle

The Uncertainty Principle

Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...

Uncertainty in Measurement: Reading Instruments

Uncertainty in Measurement: Reading Instruments

Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...

Regulated mRNA Transport

Regulated mRNA Transport

In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...

Oxidation-Reduction Reactions

Oxidation-Reduction Reactions

Oxidation–Reduction Reactions

Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

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.

You might also read

Related Articles

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

Sort by

Same author

Nano-Graphene Layer from Facile, Scalable and Eco-Friendly Liquid Phase Exfoliation Strategy as Effective Barrier Layer for High-Performance and Durable Direct Liquid Alcohol Fuel Cells.

Molecules (Basel, Switzerland)·2022

Same author

Machine learning-assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials.

Science advances·2019

Same author

A Surrogate Modelling Approach Based on Nonlinear Dimension Reduction for Uncertainty Quantification in Groundwater Flow Models.

Transport in porous media·2019

Same author

Surrogate modelling for the prediction of spatial fields based on simultaneous dimensionality reduction of high-dimensional input/output spaces.

Royal Society open science·2018

Same author

Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media.

Royal Society open science·2017

Same author

Reduced-order modelling of parameter-dependent, linear and nonlinear dynamic partial differential equation models.

Proceedings. Mathematical, physical, and engineering sciences·2017

Search research articles

Related Experiment Video

Updated: Jan 27, 2026

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

Published on: November 25, 2020

Uncertainty Quantification for Flow and Transport in Highly Heterogeneous Porous Media Based on Simultaneous

D Crevillén-García¹, P K Leung², A Rodchanarowan³

¹1School of Engineering, University of Warwick, Coventry, CV4 7AL UK.

Transport in Porous Media

|March 16, 2019

Summary

This study quantifies groundwater flow model uncertainty using Gaussian process models. It reduces model complexity for efficient uncertainty quantification in heterogeneous porous media.

Keywords:

Dimension reduction Gaussian process emulation Porous medium Spatial fields Uncertainty quantification

More Related Videos

Window on a Microworld: Simple Microfluidic Systems for Studying Microbial Transport in Porous Media

Window on a Microworld: Simple Microfluidic Systems for Studying Microbial Transport in Porous Media

Published on: May 3, 2010

Fabricating Highly Open Porous Microspheres HOPMs via Microfluidic Technology

Fabricating Highly Open Porous Microspheres HOPMs via Microfluidic Technology

Published on: May 16, 2022

Related Experiment Videos

Last Updated: Jan 27, 2026

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

Published on: November 25, 2020

Window on a Microworld: Simple Microfluidic Systems for Studying Microbial Transport in Porous Media

Window on a Microworld: Simple Microfluidic Systems for Studying Microbial Transport in Porous Media

Published on: May 3, 2010

Fabricating Highly Open Porous Microspheres HOPMs via Microfluidic Technology

Fabricating Highly Open Porous Microspheres HOPMs via Microfluidic Technology

Published on: May 16, 2022

Area of Science:

Environmental science
Hydrogeology
Computational mathematics

Background:

Groundwater flow models often face uncertainty due to random conductivity fields.
Accurate uncertainty quantification is crucial for reliable groundwater management.

Purpose of the Study:

To develop a method for quantifying uncertainty in groundwater flow models.
To reduce the dimensionality of high-dimensional input and output spaces in these models.
To build an efficient surrogate model for stochastic groundwater flow analysis.

Main Methods:

Utilized Gaussian process modeling for uncertainty quantification.
Implemented simultaneous dimension reduction in conductivity input and flow field output spaces.
Developed a reduced-order surrogate model for stochastic analysis.
Validated the surrogate model using Monte Carlo uncertainty analysis on the full-order model.

Main Results:

Successfully reduced the dimensionality of high-dimensional input and output spaces.
Retained qualitative features of the original groundwater flow model.
Developed a validated surrogate model for efficient uncertainty quantification.
Demonstrated significant reduction in computational complexity for stochastic modeling.

Conclusions:

Gaussian process models with dimension reduction offer an effective approach for groundwater flow uncertainty quantification.
The developed surrogate model provides a computationally efficient alternative for analyzing heterogeneous porous media flow.
This methodology enhances the reliability and applicability of groundwater flow models in complex scenarios.