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

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

79
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
79
Newtonian Fluid: Problem Solving01:18

Newtonian Fluid: Problem Solving

148
Newtonian fluids exhibit a constant viscosity, meaning their shear stress and shear strain rate are directly proportional. This property ensures a predictable and stable response to applied forces, maintaining a linear relationship between force and flow. Examples include water, air, and light oils, consistently demonstrating this proportional behavior regardless of external conditions.
A velocity gradient forms within the fluid when a Newtonian fluid is placed between two parallel plates, with...
148
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

58
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
58
Second Derivatives and Laplace Operator01:22

Second Derivatives and Laplace Operator

1.2K
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...
1.2K
Differential Form of Maxwell's Equations01:17

Differential Form of Maxwell's Equations

373
James Clerk Maxwell (1831–1879) was one of the significant contributors to physics in the nineteenth century. He is probably best known for having combined existing knowledge of the laws of electricity and the laws of magnetism with his insights to form a complete overarching electromagnetic theory, represented by Maxwell's equations. The four basic laws of electricity and magnetism were discovered experimentally through the work of physicists such as Oersted, Coulomb, Gauss, and...
373
Gauss's Law: Problem-Solving01:10

Gauss's Law: Problem-Solving

1.6K
Gauss's law helps determine electric fields even though the law is not directly about electric fields but electric flux. In situations with certain symmetries (spherical, cylindrical, or planar) in the charge distribution, the electric field can be deduced based on the knowledge of the electric flux. In these systems, we can find a Gaussian surface S over which the electric field has a constant magnitude. Furthermore, suppose the electric field is parallel (or antiparallel) to the area...
1.6K

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

Proximity-Driven Protein Ligation Beyond the Concentration Limit.

Journal of the American Chemical Society·2026
Same author

Quantitative Lipidomics Reveals Dynamic Lipid Profiles in <i>Cinnamomum camphora</i> Seed Kernels at Different Developmental Stages.

Plants (Basel, Switzerland)·2026
Same author

Direct Photoredox Synthesis of <i>N</i>-Linked Glycoproteins.

Journal of the American Chemical Society·2026
Same author

PRISM: Turning prediction uncertainty into cost-effective management decisions for cadmium-contaminated rice.

Journal of hazardous materials·2026
Same author

Single-cell analysis reveals an endothelial TP53-CXCL14 axis in breast cancer progression.

Journal of translational medicine·2026
Same author

Optogenetic control of plasma membrane O-GlcNAcylation regulates WNK1 condensates and cellular signaling.

Cell chemical biology·2026
Same journal

Distributionally Robust Feature Selection.

Advances in neural information processing systems·2026
Same journal

On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution.

Advances in neural information processing systems·2026
Same journal

Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time.

Advances in neural information processing systems·2026
Same journal

JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.

Advances in neural information processing systems·2026
Same journal

Learning to Route: Per-Sample Adaptive Routing for Multimodal Multitask Prediction.

Advances in neural information processing systems·2026
Same journal

Emergence and Evolution of Interpretable Concepts in Diffusion Models.

Advances in neural information processing systems·2026
查看所有相关文章

相关实验视频

Updated: May 15, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.5K

牛顿为解决非线性局部微分方程提供了神经运算符.

Wenrui Hao1, Xinliang Liu2,3, Yahong Yang1

  • 1Department of Mathematics, The Pennsylvania State University, University Park, State College, PA, USA.

Advances in neural information processing systems
|April 8, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了牛顿知情神经运算符,以高效地解决具有多个解决方案的非线性局部微分方程 (PDEs). 该方法学习了牛顿解法,减少了复杂科学问题的计算成本和数据要求.

更多相关视频

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.6K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K

相关实验视频

Last Updated: May 15, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.5K
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.6K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K

科学领域:

  • 计算数学 计算数学 计算数学
  • 科学计算科学计算
  • 数字分析 数字分析

背景情况:

  • 解决非线性局部微分方程 (PDEs) 在科学和工程领域至关重要.
  • 传统的数值方法在多个解决方案和计算费用方面扎,特别是在两叉点附近.
  • 牛顿的方法,一个常见的非线性求解器,面临的挑战是错误的问题.

研究的目的:

  • 开发一种新的方法,以多个解决方案有效地解决非线性PDEs.
  • 将传统的数值技术与神经网络相结合,以提高解决器性能.
  • 为了降低寻找多个解决方案的计算成本和数据要求.

主要方法:

  • 提出了牛顿信息的神经运算器 (NINO).
  • 在神经运算符框架内学习牛顿非线性求解器.
  • 将传统的数值方法与学习的牛顿解法器集成在一起,以实现代的改进.

主要成果:

  • 牛顿信息神经运算器有效计算非线性PDEs的多个解决方案.
  • 与现有的神经网络方法相比,该方法需要较少的监督数据点.
  • 尼诺在处理非线性解决器时展示了提高的计算效率.

结论:

  • 牛顿信息神经运算符为解决复杂的非线性PDEs提供了一种强大的新方法.
  • 这种方法解决了传统数值技术在处理多个解决方案方面的局限性.
  • 尼诺有可能加速依赖PDE解决方案的领域的研发.