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

Network Function of a Circuit01:25

Network Function of a Circuit

632
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
632
Types of Functions I01:26

Types of Functions I

236
Functions are fundamental mathematical tools that capture relationships between variables and describe how one quantity changes in relation to another. Their diverse forms allow them to model various real-world phenomena with precision and flexibility. Among the various categories, algebraic functions are prominent due to their formulation through basic arithmetic operations: addition, subtraction, multiplication, division, and root extraction.Algebraic functions include polynomial, rational,...
236
Introduction to Functions01:29

Introduction to Functions

219
Functions are essential mathematical tools used to describe consistent relationships between varying quantities. A function connects each input to a single, corresponding output based on a defined rule. These relationships appear in both everyday contexts and natural phenomena, providing a framework for understanding change and prediction.One common real-life example is a parking garage fee system, where the total cost depends on the amount of time a vehicle remains inside. In this case, the...
219
Deflection of a Beam01:19

Deflection of a Beam

677
Accurately determining beam deflection and slope under various loading conditions in structural engineering is crucial for ensuring safety and structural integrity. Singularity functions offer a streamlined approach to analyzing beams, especially when multiple loading functions complicate the bending moment equation.
Singularity functions, described in an earlier lesson, are powerful mathematical tools that represent discontinuities within a function commonly encountered in structural loading...
677
Types of Functions II01:19

Types of Functions II

166
Trigonometric and exponential functions are essential mathematical tools used to model distinct types of real-world behavior, particularly in periodic and growth-related phenomena. These functions extend the capabilities of basic algebraic models by capturing recurring cycles and rapid changes across various scientific and engineering contexts.Trigonometric functions, such as sine and cosine, are particularly effective for representing periodic phenomena. Their cyclic behavior makes them...
166
Arc Length Function01:22

Arc Length Function

5
The arc length function represents the total distance traveled along a smooth curve measured from a fixed starting point to a variable endpoint. For curves that are continuous and differentiable, arc length provides a precise way to quantify distance when straight-line approximations are insufficient.To derive arc length, the curve is divided into many small segments. Each segment is approximated by a straight line whose length depends on the horizontal and vertical changes over that interval.
5

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

Molecular dynamics study of the sonic horizon of microscopic Laval nozzles.

Physical review. E·2024
Same author

Surrogates for Liquid-Liquid Extraction.

ACS omega·2024
Same author

Discrete Modeling Approach for Cluster-Based Excess Gibbs-Energy of Molecular Liquids.

Industrial & engineering chemistry research·2023
Same author

Nonadiabatic Laser-Induced Alignment Dynamics of Molecules on a Surface.

Physical review letters·2023
Same author

Cluster-Based Thermodynamics of Interacting Dice in a Lattice.

Entropy (Basel, Switzerland)·2020
Same author

Rotational Coherence Spectroscopy of Molecules in Helium Nanodroplets: Reconciling the Time and the Frequency Domains.

Physical review letters·2020

相关实验视频

Updated: Jan 14, 2026

Functional Complementation Analysis FCA: A Laboratory Exercise Designed and Implemented to Supplement the Teaching of Biochemical Pathways
09:27

Functional Complementation Analysis FCA: A Laboratory Exercise Designed and Implemented to Supplement the Teaching of Biochemical Pathways

Published on: June 24, 2016

18.1K

桥梁功能作为辐射分布函数的函数:操作员学习和应用程序.

Martin Panholzer1, Michael Haring2, Thomas Wallek2

  • 1Uni Software Plus GmbH, Linzer Strasse 6, 4320 Perg, Austria.

Physical review. E
|October 21, 2025
PubMed
概括
此摘要是机器生成的。

机器学习预测了桥梁功能,改善了分子系统的积分方程理论. 这提高了对Lennard-Jones,Mie和硬球等流体的预测,推进了分子模拟.

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

1.5K
Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles
08:39

Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles

Published on: October 16, 2017

13.2K

相关实验视频

Last Updated: Jan 14, 2026

Functional Complementation Analysis FCA: A Laboratory Exercise Designed and Implemented to Supplement the Teaching of Biochemical Pathways
09:27

Functional Complementation Analysis FCA: A Laboratory Exercise Designed and Implemented to Supplement the Teaching of Biochemical Pathways

Published on: June 24, 2016

18.1K
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

1.5K
Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles
08:39

Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles

Published on: October 16, 2017

13.2K

科学领域:

  • 计算物理学的计算物理.
  • 统计力学就是统计力学.
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 整方程理论,如奥恩斯坦-泽尼克方程,对于计算分子系统属性至关重要.
  • 超联网链 (HNC) 近似,一个常见的关闭关系,忽略了桥梁功能,限制了它的准确性.

研究的目的:

  • 开发一种机器学习方法来预测桥梁功能.
  • 通过结合神经网络预测的桥梁函数来提高积分方程理论的准确性.

主要方法:

  • 使用蒙特卡洛模拟伦纳德-斯流体的桥梁函数训练了一个深度操作员网络.
  • 经过训练的网络根据辐射分布函数预测桥梁功能.
  • 改进的HNC关闭,包括预测的桥梁功能,被代地解决.

主要成果:

  • 与标准HNC相比,基于神经网络的桥梁功能显著改善了辐射分布函数和压力的预测.
  • 该方法证明了普遍性,显示了Mie和硬球体流体的良好一致性,使用在Lennard-Jones数据上训练的模型.

结论:

  • 机器学习提供了一个强大的工具,可以在分子模拟中增强积分方程理论.
  • 拟议的方法为研究古典分子系统提供了更准确和更通用的方法.