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

267
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...
267
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

230
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...
230
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.8K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.8K
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

339
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...
339
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

226
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...
226
Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

4.1K
The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
4.1K

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

Overlap locking and nonperturbative effects in spin glasses.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Demonstrating real advantage of machine learning-enhanced Monte Carlo for combinatorial optimization.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Rare trajectories in a prototypical mean-field disordered model: Insights into landscape and instantons.

Physical review. E·2026
Same author

Functional bottlenecks can emerge from non-epistatic underlying traits.

PLoS computational biology·2026
Same author

Interacting copies of random-constraint satisfaction problems.

Physical review. E·2026
Same author

Strong Ergodicity Breaking in Dynamical Mean-Field Equations for Mixed p-Spin Glasses.

Physical review letters·2026

相关实验视频

Updated: Jan 11, 2026

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

5.0K

机器学习辅助的蒙特卡洛在从简单的统计物理模型采样中的性能.

Luca Maria Del Bono1,2, Federico Ricci-Tersenghi1,2,3, Francesco Zamponi1

  • 1Sapienza Università di Roma, Dipartimento di Fisica, Piazzale Aldo Moro 5, Rome 00185, Italy.

Physical review. E
|November 18, 2025
PubMed
概括
此摘要是机器生成的。

机器学习有助于进行难以采样的模拟. 这项研究分析了Curie-Weiss模型的MADE架构的全球化,为蒙特卡洛采样优化提供了理论见解.

更多相关视频

Setting Limits on Supersymmetry Using Simplified Models
07:46

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

8.9K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.5K

相关实验视频

Last Updated: Jan 11, 2026

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

5.0K
Setting Limits on Supersymmetry Using Simplified Models
07:46

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

8.9K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.5K

科学领域:

  • 计算物理 计算物理
  • 机器学习应用 机器学习应用
  • 统计力学 统计力学

背景情况:

  • 传统的方法与难以采样的系统作斗争.
  • 机器学习提供了新的模拟方法.
  • 对这些方法的理论理解是有限的.

研究的目的:

  • 用MADE架构提供全球化 (序列化) 的理论分析.
  • 了解最佳训练和重量,以优化梯度下降.
  • 为了比较全球化与和没有本地大都市蒙特卡洛步骤.

主要方法:

  • 全球退火过程的分析研究.
  • 适用于浅层MADE架构的应用.
  • 库里-韦斯模型模拟.

主要成果:

  • 描述最佳的重量和梯度下降训练.
  • 全球和没有本地蒙特卡洛步骤的全球结的比较.
  • 对这个系统的最佳程序的理论见解.

结论:

  • 建立了将机器学习整合到蒙特卡洛采样中的理论基础.
  • 为优化机器学习辅助模拟提供指导.
  • 强调理论理解对于有效实施的重要性.