Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

103
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...
103
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

89
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...
89
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

131
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...
131
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

260
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,...
260
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

729
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...
729
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

170
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
170

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

General oblique projections for model reduction via spectral submanifolds.

Chaos (Woodbury, N.Y.)·2026
Same author

Data-driven nonlinear model reduction to spectral submanifolds via oblique projection.

Chaos (Woodbury, N.Y.)·2025
Same author

Data-driven linearization of dynamical systems.

Nonlinear dynamics·2024
Same author

Nonautonomous spectral submanifolds for model reduction of nonlinear mechanical systems under parametric resonance.

Chaos (Woodbury, N.Y.)·2024
Same author

Nonlinear model reduction to temporally aperiodic spectral submanifolds.

Chaos (Woodbury, N.Y.)·2024
Same author

Data-driven modeling and forecasting of chaotic dynamics on inertial manifolds constructed as spectral submanifolds.

Chaos (Woodbury, N.Y.)·2024
Same journal

PCSK5 promotes angiogenesis and cardiac repair after myocardial infarction.

Nature communications·2026
Same journal

PfApiAT2 is a proline transporter essential for the transmission of Plasmodium falciparum by the mosquito vector.

Nature communications·2026
Same journal

Transient distortions of the South Atlantic Anomaly radiation environments driven by electric fields.

Nature communications·2026
Same journal

Structural basis of the regulation by CDK11 kinase of early spliceosome activation and evidence for its proofreading by DHX15 helicase.

Nature communications·2026
Same journal

Structural and mechanistic insights into primer synthesis initiation by DNA primase.

Nature communications·2026
Same journal

Changes in heritability and shared environmentality of educational attainment across twentieth-century Norway.

Nature communications·2026
查看所有相关文章

相关实验视频

Updated: Sep 17, 2025

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

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

Published on: February 13, 2021

6.6K

全球化对方程和数据的基于多元组的缩小模型.

Bálint Kaszás1, George Haller2

  • 1Institute for Mechanical Systems, ETH Zürich, Zurich, Switzerland. bkaszas@ethz.ch.

Nature communications
|July 1, 2025
PubMed
概括
此摘要是机器生成的。

这项研究通过使用帕德近似值来增强非线性模型的减少,以扩展不变的多元组分析超出局部多项式近似值. 这种方法使动态系统中复杂的全球现象能够更准确地进行简化建模.

更多相关视频

A Rapid Method for Modeling a Variable Cycle Engine
04:58

A Rapid Method for Modeling a Variable Cycle Engine

Published on: August 13, 2019

7.7K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

相关实验视频

Last Updated: Sep 17, 2025

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

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

Published on: February 13, 2021

6.6K
A Rapid Method for Modeling a Variable Cycle Engine
04:58

A Rapid Method for Modeling a Variable Cycle Engine

Published on: August 13, 2019

7.7K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

科学领域:

  • 应用数学 应用数学 应用数学
  • 计算力学 计算力学 计算力学
  • 动态系统理论 动态系统理论

背景情况:

  • 模型缩小对于分析复杂的动态系统至关重要.
  • 使用不变变种类的当前方法受到局部多项式近似和收域的限制.
  • 严格的非线性模型还原依赖于识别吸引不变的多元体.

研究的目的:

  • 为了克服局部多项式近似的局限性,在不变的多元体识别.
  • 扩大基于多元组的模型缩小技术的适用性.
  • 为了使复杂系统中全球现象的简化建模.

主要方法:

  • 使用帕德近似值,扩展局部扩展对不变的多元体.
  • 为了更广泛的实用性,重新表达泰勒扩展作为理性函数.
  • 将全球化的基于多元组的模型缩减应用于方程和数据驱动的示例.

主要成果:

  • 帕德近似值显著扩大了局部扩展对不变变频谱的实用性.
  • 增强的方法扩大了多重缩小模型的适用性.
  • 成功应用于固体和流体力学实例,包括大规模振荡和混乱吸引器.

结论:

  • 基于帕德近似数的多元扩展为非线性模型减少提供了一个数学严格的方法.
  • 这种方法提高了准确建模全球现象的能力.
  • 这种方法在力学中有效用于方程驱动和数据驱动的建模.