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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

150
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...
150
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

152
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
152
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

852
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...
852
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

325
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,...
325
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

119
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
119
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

291
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
291

You might also read

Related Articles

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

Sort by
Same author

Thickness-Dependent Macroscopic Properties of Highly Filled Composite Elastomers: Role of Hierarchical Filler Network and Viscoelastic Behavior.

Nano letters·2025
Same author

MetaKSSD: boosting the scalability of the reference taxonomic marker database and the performance of metagenomic profiling using sketch operations.

Nature computational science·2025
Same author

Knowledge, Attitude, and Practice Regarding Digital Dental Technologies Among Dentists in Jiangsu Province.

Healthcare (Basel, Switzerland)·2025
Same author

Kssdtree: an interactive Python package for phylogenetic analysis based on sketching technique.

Bioinformatics (Oxford, England)·2024
Same author

Data-Driven Framework toward Accurate Prediction of Interfacial Thermal Resistance in Particulate-Filled Composites.

ACS applied materials & interfaces·2023
Same author

Methyltransferase-like 3 facilitates lung cancer progression by accelerating m6A methylation-mediated primary miR-663 processing and impeding SOCS6 expression.

Journal of cancer research and clinical oncology·2022
Same journal

Correction: Yang et al. Microstructural Characteristics of High-Pressure Die Casting with High Strength-Ductility Synergy Properties: A Review. <i>Materials</i> 2023, <i>16</i>, 1954.

Materials (Basel, Switzerland)·2026
Same journal

Effect of La and Ce Microalloying on the Corrosion Resistance of 0.4Sb Low-Alloy Steel in a Harsh Marine Atmospheric Environment.

Materials (Basel, Switzerland)·2026
Same journal

High-Temperature Properties of Magnesium Ammonium Phosphate Cement Modified with Gold Tailings.

Materials (Basel, Switzerland)·2026
Same journal

A Study on the Evolution of Intermetallic Phase Microstructure and High-Temperature Creep Behavior in Mg-8.0Al-1.0Nd-1.5Gd-Mn Alloys.

Materials (Basel, Switzerland)·2026
Same journal

Material-Driven Clinical Complications in Mechanical Circulatory Support: From Blood-Material Interactions to Device-Related Adverse Events.

Materials (Basel, Switzerland)·2026
Same journal

Influence of Final Irrigation on Calcium Silicate-Based Sealer Dentinal Tubular Penetration: A Systematic Review.

Materials (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Nov 3, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.0K

A Stochastic FE2 Data-Driven Method for Nonlinear Multiscale Modeling.

Xiaoxin Lu1, Julien Yvonnet2, Leonidas Papadopoulos3

  • 1Shenzhen Institute of advanced electronic materials, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518103, China.

Materials (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

A novel machine learning approach accelerates complex nonlinear multiscale simulations. This data-driven method significantly reduces computation time for analyzing random heterogeneous structures and propagating uncertainties.

Keywords:
data-drivenmultiscaleneural networksnonlinearstochastics

More Related Videos

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials
11:28

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

Published on: May 18, 2015

12.7K
Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces
06:14

Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces

Published on: September 11, 2018

6.8K

Related Experiment Videos

Last Updated: Nov 3, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.0K
A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials
11:28

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

Published on: May 18, 2015

12.7K
Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces
06:14

Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces

Published on: September 11, 2018

6.8K

Area of Science:

  • Computational mechanics
  • Materials science
  • Machine learning

Background:

  • Multiscale modeling is crucial for understanding heterogeneous materials.
  • Traditional methods for nonlinear multiscale calculations are computationally intensive.
  • Accurate surrogate models are needed to reduce computational cost.

Purpose of the Study:

  • To introduce a stochastic data-driven multilevel finite-element (FE2) method for random nonlinear multiscale calculations.
  • To develop a hybrid neural-network-interpolation (NN-I) scheme for constructing surrogate models.
  • To demonstrate the application of this machine learning method for uncertainty quantification.

Main Methods:

  • A hybrid neural-network-interpolation (NN-I) scheme was developed to create a surrogate model for macroscopic nonlinear constitutive laws.
  • Representative volume element calculations provided input data for the surrogate model.
  • The developed FE2 method integrates the NN-I scheme to replace direct nonlinear multiscale calculations.

Main Results:

  • The NN-I scheme enhanced surrogate model accuracy, especially with limited data.
  • Computational time was reduced by several orders of magnitude compared to direct FE2.
  • The method enabled Monte Carlo simulations for uncertainty propagation in nonlinear heterogeneous structures.

Conclusions:

  • The proposed data-driven FE2 method offers a computationally efficient approach for nonlinear multiscale problems.
  • This machine learning technique facilitates uncertainty quantification and probabilistic model identification.
  • Successful application to nonlinear electric conduction in graphene-polymer composites demonstrates its practical utility.