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

Rapidly Varying Flow01:24

Rapidly Varying Flow

451
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
451
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

726
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
726
Gradually Varying Flow01:29

Gradually Varying Flow

411
Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
411
Bernoulli's Equation for Flow Along a Streamline01:30

Bernoulli's Equation for Flow Along a Streamline

1.4K
Bernoulli's equation relates the energy conservation in a fluid moving along a streamline. The equation applies to incompressible and inviscid fluids under steady flow. For such a flow, Newton's second law is applied to a small fluid element, which experiences forces due to pressure differences, gravity, and velocity variations. The force balance leads to the following form of Bernoulli's equation:
1.4K
Bernoulli's Equation for Flow Normal to a Streamline01:16

Bernoulli's Equation for Flow Normal to a Streamline

1.3K
Bernoulli's equation for flow normal to a streamline explains how pressure varies across curved streamlines due to the outward centrifugal forces induced by the fluid's curvature. The pressure is higher on the inner side of the curve, near the center of curvature, and decreases outward to balance these centrifugal forces.
The pressure difference depends on the fluid's velocity and radius of curvature. The pressure variation is minimal in flows with nearly straight streamlines. However, the...
1.3K
Autoregulation of Blood Flow01:17

Autoregulation of Blood Flow

7.5K
Autoregulation mechanisms are characterized by their inherent capacity for self-regulation without necessitating specific nervous stimulation or endocrine control. These mechanisms facilitate the adjustment of blood flow and, therefore, perfusion specific to each tissue region. This self-regulation encompasses chemical signals and myogenic controls.
Chemical Signaling in Autoregulation
Chemical signaling operates at the precapillary sphincter level, inciting either contraction or relaxation....
7.5K

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

Pseudocapsule status combined with pathological parameters predicts prognosis in renal cell carcinoma.

Frontiers in oncology·2026
Same author

Integrated Network Pharmacology, Transcriptomics, and Experimental Validation Identify PI3K-AKT and STAT3 as Key Pathways for Pien Tze Huang Against Liver and Colorectal Cancers.

Current pharmaceutical design·2026
Same author

Impact of a Pharmacist-Led Grid-Based Stewardship Program on the Association Between Antimicrobial Use and Resistance: A 7.5-Year Interrupted Time-Series Analysis in a Chinese Tertiary Hospital.

Infection and drug resistance·2026
Same author

Surface Glass Fiber Hybridization for Enhanced Low-Velocity Impact Resistance in CFRP T-Stiffened Panels.

Polymers·2026
Same author

Interventions to reduce occupational burnout in general practitioners: a systematic review and meta-analysis protocol.

Frontiers in public health·2026
Same author

Effectiveness of Balint group interventions for physician burnout: a systematic review and meta-analysis protocol.

BMJ open·2026

相关实验视频

Updated: Jan 16, 2026

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

12.8K

一种基于神经自回流的神经自回流的准蒙特卡洛方法.

Yunfan Wei1, Wei Xi2

  • 1School of Mathematics, South China University of Technology, Guangzhou 510641, China.

Entropy (Basel, Switzerland)
|September 27, 2025
PubMed
概括

本研究介绍了一个运输准蒙特卡洛 (TQMC) 框架,使用神经自回流来进行高效的高维取样和集成. 在金融建模中,TQMC提高了准确性和预测性能.

科学领域:

  • 计算统计学 计算统计学
  • 机器学习 机器学习
  • 金融数学 金融数学

背景情况:

  • 高维集成和采样是计算密集的.
  • 现有的方法,如标准的蒙特卡洛和规范化流程,在效率和准确性方面存在局限性.
  • 接近复杂的概率分布对于各种科学和金融应用至关重要.

研究的目的:

  • 开发一个新的运输准蒙特卡洛 (TQMC) 框架,以有效采样和整合.
  • 将随机的准蒙特卡洛抽样与神经自回流结合起来.
  • 证明该框架在准确性和效率方面比现有方法具有更高的性能.

主要方法:

  • 构建了一系列可逆的运输图,以近似目标密度.
  • 通过将复杂分布分解为更低维的边缘值来实现近似.
  • 通过单调的β-平均转换来参数化的规范化流被利用,通过前进的Kullback-Leibler分歧进行优化.
  • 一个隐藏变量机制在子模型之间传输参数,以提高计算效率.

主要成果:

  • 对香形分布的数值实验表明,TQMC在采样准确性和整数估计方面优于基于蒙特卡洛的标准规范化流量.
  • 对A股股票回报率数据的应用在半年回报率预测中显示出可靠的预测性表现.
关键词:
自动回归模型的自动回归模型为了使流量正常化.几乎是蒙特卡洛的样子.股票回报率预测 预测运输地图 运输地图 运输地图

更多相关视频

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro
08:00

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Published on: December 3, 2018

8.8K
Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

6.2K

相关实验视频

Last Updated: Jan 16, 2026

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

12.8K
Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro
08:00

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Published on: December 3, 2018

8.8K
Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

6.2K
  • 该模型准确地捕捉了跨资产的协差结构,表明其在金融建模中的实用性.
  • 结论:

    • 拟议的运输准蒙特卡洛框架在采样和整合效率和准确性方面提供了显著的改进.
    • 在金融建模中,TQMC具有很强的应用潜力,特别是在回报预测和协差估计中.
    • 这种新的方法推进了高维推理和统计建模领域.