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

Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

623
Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
623
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

95
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
95
Second Derivatives and Laplace Operator01:22

Second Derivatives and Laplace Operator

1.3K
The first order operators using the del operator include the gradient, divergence and curl. Certain combinations of first order operators on a scalar or vector function yield second order expressions. Second-order expressions play a very important role in mathematics and physics. Some second order expressions include the divergence and curl of a gradient function, the divergence and curl of a curl function, and the gradient of a divergence function.
Consider a scalar function. The curl of its...
1.3K
Laminar Flow: Problem Solving01:24

Laminar Flow: Problem Solving

223
Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
223
Two-Dimensional Force System01:20

Two-Dimensional Force System

953
A two-dimensional system in mechanical engineering involves the analysis of motion and forces in a plane. A two-dimensional force vector can be resolved into its components as:
953
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

696
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
696

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

Diversity of Root System Architecture in Mediterranean Maize Inbred Lines Provides New Breeding Opportunities to Improve Stress Resilience and Resource Efficiency.

Plants (Basel, Switzerland)·2026
Same author

Automated generation of ground truth images of greenhouse-grown plant shoots using a GAN approach.

Plant methods·2025
Same author

Computational Simulation of LAVA Treatment of Thyroid Eye Disease Predicts Soft Tissue Outcome Comparable to Two-Wall Resection.

Bioengineering (Basel, Switzerland)·2025
Same author

Linel2D-Net: A deep learning approach to solving 2D linear elastic boundary value problems on image domains.

iScience·2024
Same author

High-Throughput Spike Detection in Greenhouse Cultivated Grain Crops with Attention Mechanisms-Based Deep Learning Models.

Plant phenomics (Washington, D.C.)·2024
Same author

Awn Image Analysis and Phenotyping Using BarbNet.

Plant phenomics (Washington, D.C.)·2024

相关实验视频

Updated: Jul 27, 2025

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

13.7K

FDM数据驱动的U-Net作为一个2D拉普拉斯PINN解决器.

Anto Nivin Maria Antony1, Narendra Narisetti2, Evgeny Gladilin3

  • 1Leibniz Institute of Plant Genetics and Crop Plant Research, OT Gatersleben, Corrensstr. 3, 06466, Seeland, Germany. maria@ipk-gatersleben.de.

Scientific reports
|June 5, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的深度学习方法,用于解决部分微分方程 (PDEs). 物理信息神经网络 (PINN) 方法为二维拉普拉斯方程提供高精度的高效,近实时的解决方案.

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

447
Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

12.3K

相关实验视频

Last Updated: Jul 27, 2025

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

13.7K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

447
Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

12.3K

科学领域:

  • 计算数学 计算数学 计算数学
  • 机器学习 机器学习
  • 图像分析 图像分析

背景情况:

  • 解决部分微分方程 (PDEs) 的传统数值方法,如有限差异 (FDM) 和有限元素 (FEM),在计算上是密集的,并且很难适应新的应用.
  • 物理信息神经网络 (PINNs) 已经成为一个有前途的替代方案,为解决PDEs提供了更简单的应用和潜在的更好的性能.

研究的目的:

  • 开发和评估一种新的数据驱动方法来解决2D拉普拉斯局部微分方程 (PDE) 的任意边界条件.
  • 为了证明深度学习模型的有效性,特别是PINNs,训练在有限差异方法 (FDM) 解决方案的前向和反向PDE问题.

主要方法:

  • 开发了一个使用物理信息神经网络 (PINNs) 的深度学习框架.
  • 通过有限差异方法 (FDM) 产生的参考解决方案的综合数据集来训练PINN模型.
  • 该方法在2D拉普拉斯方程的各种边界值问题上进行了测试.

主要成果:

  • 拟议的PINN方法实现了几乎实时的性能来解决二维拉普拉斯PDE.
  • 在不同边界条件下将PINN解决方案与FDM结果进行比较时,获得了94%的平均准确性.
  • 使用数据驱动的深度学习方法,前向和反向的2D拉普拉斯问题都得到了高效的解决.

结论:

  • 开发的基于深度学习的PINN解决器为解决二维拉普拉斯PDE提供了一个高效和准确的工具.
  • 这种方法显示出在图像分析和基于图像的边界条件的物理问题的计算模拟应用的巨大潜力.