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

Signal Flow Graphs01:18

Signal Flow Graphs

321
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
321
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

150
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
150
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
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.3K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.3K
Modeling in Therapy01:26

Modeling in Therapy

153
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
153
Relation between Mathematical Equations and Block Diagrams01:20

Relation between Mathematical Equations and Block Diagrams

1.1K
In a spring-mass-damper system, the second-order differential equation describes the dynamic behavior of the system. When transformed into the Laplace domain under zero initial conditions, this equation can be effectively analyzed and manipulated. The transformation into the Laplace domain converts differential equations into algebraic equations, simplifying the process of isolating the output.
1.1K

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

Force-Field Optimization by End-to-End Differentiable Atomistic Simulation.

Journal of chemical theory and computation·2025
Same author

Topological controls on the dissolution kinetics of glassy aluminosilicates.

Journal of the American Ceramic Society. American Ceramic Society·2024
Same author

Investigation of Carbonation Kinetics in Carbonated Cementitious Materials by Reactive Molecular Dynamics Simulations.

ACS sustainable chemistry & engineering·2024
Same author

Effects of temperature and CO2 concentration on the early stage nucleation of calcium carbonate by reactive molecular dynamics simulations.

The Journal of chemical physics·2024
Same author

Complex dislocation loop networks as natural extensions of the sink efficiency of saturated grain boundaries in irradiated metals.

Science advances·2024
Same author

Deciphering the controlling factors for phase transitions in zeolitic imidazolate frameworks.

National science review·2024

相关实验视频

Updated: Sep 17, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

751

使用图形神经网络和符号回归来建模无序系统.

Ruoxia Chen1, Mathieu Bauchy2, Wei Wang3

  • 1Physics of AmoRphous and Inorganic Solids Laboratory (PARISlab), Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, USA. ruoxia@g.ucla.edu.

Scientific reports
|July 2, 2025
PubMed
概括

本研究引入了一种新的机器学习方法,用于在无序系统中精确的原子间潜在能计算. 这种方法通过克服先前知识和高计算成本的传统局限性来增强分子动力学模拟.

关键词:
实力场是一个力场.机器学习是机器学习.分子动态模拟的分子动态模拟.

更多相关视频

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.2K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

592

相关实验视频

Last Updated: Sep 17, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

751
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.2K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

592

科学领域:

  • 计算物理学的计算物理.
  • 材料科学是一种材料科学.
  • 化学物理 化学物理

背景情况:

  • 准确模拟原子轨迹对于使用分子动力学 (MD) 建模无序系统至关重要.
  • MD模拟的精度取决于原子间电位函数,该函数控制着原子运动计算.
  • 导出原子间潜力的传统方法是知识密集型和计算昂贵的.

研究的目的:

  • 为无序系统引入一种新的方法,将机器学习与分子动力学相结合.
  • 提供精确的原子间潜在能量计算,提高模拟精度.
  • 解决传统的原子间潜能导出方法的局限性.

主要方法:

  • 机器学习算法的集成与分子动力学模拟.
  • 开发一种用于潜在能源计算的新型计算框架.
  • 适用于需要精确的原子轨迹模拟的无序系统.

主要成果:

  • 实现了对无序系统的精确原子间潜在能量计算.
  • 证明了机器学习综合方法的有效性.
  • 克服了对广泛的先前物理知识的需求,并降低了计算成本.

结论:

  • 这种新的机器学习辅助方法提供了一种更有效,更准确的方法来建模无序系统.
  • 这种方法提升了分子动力学模拟的功能.
  • 它为更复杂的材料和现象的更复杂的建模铺平了道路.