Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

952
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
952
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

267
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
267
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

188
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
188
Navier–Stokes Equations01:28

Navier–Stokes Equations

1.5K
For incompressible Newtonian fluids, where density remains constant, stresses show a linear relationship with the deformation rate, defined by normal and shear stresses. Normal stresses depend on the pressure exerted on the fluid and the rate of deformation in specific directions, which determines how fluid flows under varying pressures. Shear stresses, on the other hand, act tangentially across fluid layers. They explain how adjacent fluid layers slide relative to one another, connecting...
1.5K
Differential Form of Maxwell's Equations01:17

Differential Form of Maxwell's Equations

975
James Clerk Maxwell (1831–1879) was one of the significant contributors to physics in the nineteenth century. He is probably best known for having combined existing knowledge of the laws of electricity and the laws of magnetism with his insights to form a complete overarching electromagnetic theory, represented by Maxwell's equations. The four basic laws of electricity and magnetism were discovered experimentally through the work of physicists such as Oersted, Coulomb, Gauss, and...
975
Typical Model Studies01:30

Typical Model Studies

528
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
528

You might also read

Related Articles

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

Sort by
Same author

Agentic and LLM-Based Multimodal Anomaly Detection: Architectures, Challenges, and Prospects.

Sensors (Basel, Switzerland)·2026
Same author

Adaptive Bayesian learning for stability characterization of re-entry vehicles.

Scientific reports·2026
Same author

Macrophages in human atherosclerotic plaques in the era of single-cell and spatial transcriptomics.

ImmunoHorizons·2026
Same author

Editorial: Metabolism in the tumour microenvironment: implications for pathogenesis and therapeutics.

Frontiers in immunology·2026
Same author

The evolution of digital twins from reactive to agentic systems.

Nature computational science·2026
Same author

Digital twin syncing for autonomous surface vessels using reinforcement learning and nonlinear model predictive control.

Scientific reports·2025

Related Experiment Video

Updated: Nov 29, 2025

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
13:04

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation

Published on: January 18, 2022

4.5K

Forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction.

Shady E Ahmed1, Kinjal Bhar1, Omer San1

  • 1School of Mechanical & Aerospace Engineering, Oklahoma State University, Stillwater, Oklahoma 74078, USA.

Physical Review. E
|November 20, 2020
PubMed
Summary

This study introduces a new method to estimate eddy viscosity for reduced order models using forward sensitivity analysis (FSM). This approach improves model accuracy by assimilating sparse or full sensor data, benefiting digital twins.

More Related Videos

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

8.2K
Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
13:07

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

Published on: January 15, 2022

4.2K

Related Experiment Videos

Last Updated: Nov 29, 2025

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
13:04

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation

Published on: January 18, 2022

4.5K
Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

8.2K
Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
13:07

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

Published on: January 15, 2022

4.2K

Area of Science:

  • Computational Fluid Dynamics
  • Data Assimilation
  • Model Order Reduction

Background:

  • Nonlinear reduced order models (ROMs) often require accurate closure modeling for eddy viscosity.
  • Standard Galerkin reduced order models (GROMs) can suffer from prediction errors due to inadequate parameterization.
  • Data assimilation techniques are crucial for correcting model discrepancies with observations.

Purpose of the Study:

  • To develop and validate a variational approach for estimating eddy viscosity in nonlinear ROMs.
  • To leverage the forward sensitivity method (FSM) for data assimilation in ROM closure modeling.
  • To assess the effectiveness of the proposed method across different measurement configurations and complex fluid dynamics problems.

Main Methods:

  • Implementation of a variational approach combined with the forward sensitivity method (FSM).
  • Application to projection-based ROMs of the 1D viscous Burgers equation and the 2D vorticity transport equation.
  • Investigation of eddy viscosity estimation using both full-field and sparse noisy measurements.

Main Results:

  • The proposed GROM-FSM framework successfully estimates optimal eddy viscosity values.
  • The method effectively assimilates information from various measurement types to correct GROM predictions.
  • Accurate eddy viscosity closure is achieved, significantly improving model forecasts.

Conclusions:

  • The GROM-FSM framework offers a modular solution for correcting forecasting errors in ROMs using observational data.
  • This approach is highly effective even with sparse measurements on a latent space.
  • The framework shows significant promise for real-time parameter optimization in digital twin applications.