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

Second Derivatives and Laplace Operator01:22

Second Derivatives and Laplace Operator

2.6K
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...
2.6K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

328
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....
328
Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

4.1K
The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
4.1K
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

310
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,...
310
Second Order systems II01:18

Second Order systems II

364
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
364
Definition of Laplace Transform01:22

Definition of Laplace Transform

4.2K
The Laplace transform is an indispensable mathematical technique for simplifying the resolution of differential equations by converting them into more manageable algebraic expressions. The Laplace transform of a function is denoted by L[x(t)], where x(t) is the time-domain function. The laplace transform is mathematically expressed as
4.2K

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

Learning patient-specific spatial biomarker dynamics via operator learning for Alzheimer's disease progression.

NPJ systems biology and applications·2026
Same author

The triangular drivers of bone aging: mechanistic insights and therapeutic targets in cellular senescence, estrogen deficiency, and gut microenvironment dysregulation.

Frontiers in cell and developmental biology·2026
Same author

Optimal error estimates of the diffuse domain method for second order parabolic equations.

BIT. Numerical mathematics·2026
Same author

General scales unlock AI evaluation with explanatory and predictive power.

Nature·2026
Same author

Unveiling Scaling Laws of Parameter Identifiability and Uncertainty Quantification in Data-Driven Biological Modeling.

ArXiv·2026
Same author

Interfacial Characteristics of HgCdTe Infrared Detectors Grown on Alternative Substrates.

Sensors (Basel, Switzerland)·2026
Same journal

HeartSimSage: Attention-Enhanced Graph Neural Networks for Accelerating Cardiac Mechanics Modeling.

Journal of computational physics·2026
Same journal

Composite B-spline regularized delta functions for the immersed boundary method: Divergence-free interpolation and gradient-preserving force spreading.

Journal of computational physics·2026
Same journal

Improving the robustness of the immersed interface method through regularized velocity reconstruction.

Journal of computational physics·2025
Same journal

An efficient adaptive algorithm for photon-electron coupled Boltzmann equation in radiation therapy.

Journal of computational physics·2025
Same journal

On generalizing the induced surface charge method to heterogeneous Poisson-Boltzmann models for electrostatic free energy calculation.

Journal of computational physics·2025
Same journal

A boundary integral method for simulating the dynamics of inextensible vesicles suspended in a viscous fluid in 2D.

Journal of computational physics·2025
查看所有相关文章

相关实验视频

Updated: Jan 7, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

467

拉普拉斯基基于自身功能的神经运算器,用于学习非线性反应-扩散动力学.

Jindong Wang1, Wenrui Hao1

  • 1Department of Mathematics, Penn State University, University Park, 16802, PA, USA.

Journal of computational physics
|December 12, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了拉普拉斯的基于自身功能的神经运算符 (LE-NO),用于学习反应-扩散方程. 通过使用光谱表示,LE-NO有效地模拟非线性术语,提高计算效率和科学发现的数据处理.

关键词:
拉普拉斯的固有函数数据驱动的PDE发现.非线性反应-扩散问题运营商学习 运营商学习基于物理的机器学习.

更多相关视频

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

11.9K
Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

23.7K

相关实验视频

Last Updated: Jan 7, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

467
Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

11.9K
Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

23.7K

科学领域:

  • 科学计算是科学计算.
  • 数学物理学的数学物理.
  • 数据驱动的建模.

背景情况:

  • 反应-扩散方程在诸如流体动力学,材料科学和生物学等多个领域都至关重要.
  • 学习这些复杂的系统往往面临着计算成本和数据要求的挑战.

研究的目的:

  • 开发一种新的框架,以有效地学习反应-扩散方程中的非线性反应项.
  • 解决操作员学习的局限性,例如数据稀缺性和大型模型大小.

主要方法:

  • 提出了拉普拉斯的基于自身功能的神经运营者 (LE-NO) 框架.
  • 利用拉普拉斯的固有函数作为模拟非线性运算符的光谱基础.
  • 杆直接矩阵反转用于计算效率.

主要成果:

  • LE-NO证明了非线性项的有效近似.
  • 与传统方法相比,该框架显示了计算复杂性的降低.
  • LE-NO在不同的边界条件中得到了很好的概括,并提供了可解释的动态.

结论:

  • LE-NO为发现和预测反应-扩散动态提供了一个强大而稳健的工具.
  • 光谱方法有效地捕捉了数学物理中的复杂非线性行为.
  • 这种方法减轻了操作员学习中的常见挑战,提高了适用性.