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

Typical Model Studies01:30

Typical Model Studies

354
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
354
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

183
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:
183
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
48
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

81
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,...
81
Modeling and Similitude01:12

Modeling and Similitude

261
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
261
Mechanical Systems01:22

Mechanical Systems

190
Mechanical systems are analogous to to electrical networks where springs and masses play similar roles to inductors and capacitors, respectively. A viscous damper in mechanical systems functions similarly to a resistor in electrical networks, dissipating energy. The forces acting on a mass in such systems include an applied force in the direction of motion, counteracted by forces from the spring, a viscous damper, and the mass's acceleration. This interplay of forces is mathematically...
190

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

Real-time CBCT reconstructions using Krylov solvers in repeated scanning procedures.

Physics in medicine and biology·2026
Same author

Artificial Intelligence-Led Whole Coronary Artery OCT Analysis; Validation and Identification of Drug Efficacy and Higher-Risk Plaques.

Circulation. Cardiovascular imaging·2025
Same author

Partial differential equations in data science.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2025
Same author

Enhancing Brain Age Prediction and Neurodegeneration Detection with Contrastive Learning on Regional Biomechanical Properties.

bioRxiv : the preprint server for biology·2025
Same author

Extracting chain lines and laid lines from digital images of medieval paper using spectral total variation decomposition.

Heritage science·2023
Same author

On Krylov methods for large-scale CBCT reconstruction.

Physics in medicine and biology·2023

相关实验视频

Updated: Jun 22, 2025

Finite Element Modelling of a Cellular Electric Microenvironment
08:23

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

3.4K

基于物理学的神经网络能否击败有限元法?

Tamara G Grossmann1, Urszula Julia Komorowska2, Jonas Latz3

  • 1Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK.

IMA journal of applied mathematics
|June 27, 2024
PubMed
概括

本研究比较了解决部分微分方程 (PDEs) 的数值方法. 没有发现物理信息神经网络 (PINNs) 在准确性和速度方面优于已建立的有限元素方法 (FEM).

关键词:
深度学习是一种深度学习.有限元素方法的有限元素方法.部分微分方程部分微分方程.基于物理学的神经网络.

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

1.7K
Finite Element Modeling for the Simulation of the Quasi-Static Compression of Corrugated Tapered Tubes
06:34

Finite Element Modeling for the Simulation of the Quasi-Static Compression of Corrugated Tapered Tubes

Published on: January 6, 2023

1.6K

相关实验视频

Last Updated: Jun 22, 2025

Finite Element Modelling of a Cellular Electric Microenvironment
08:23

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

3.4K
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

1.7K
Finite Element Modeling for the Simulation of the Quasi-Static Compression of Corrugated Tapered Tubes
06:34

Finite Element Modeling for the Simulation of the Quasi-Static Compression of Corrugated Tapered Tubes

Published on: January 6, 2023

1.6K

科学领域:

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

背景情况:

  • 部分微分方程 (PDEs) 对于科学现象的建模至关重要.
  • 数字方法用于模拟PDE解决方案的近似值.
  • 基于物理学的神经网络 (PINNs) 是PDEs的最新深度学习方法.

研究的目的:

  • 系统地比较PINNs和有限元法 (FEM) 的性能.
  • 在各种 PDE 上评估这两种方法的计算成本和近似精度.

主要方法:

  • 解决一维,二维和三维的波桑方程.
  • 解决1D艾伦-卡恩和1D/2D半线性施罗丁格方程.
  • 使用PINNs和FEM进行数值近似.

主要成果:

  • 与PINNs相比,FEM通常实现了更高的准确性和解决时间.
  • 在特定的实验设置中,PINNs证明了解决的PDE的更快评估.
  • 在这项比较研究中,没有发现PINNs与FEM相比具有显著的优势.

结论:

  • 有限元法仍然是解决广泛的PDE的可靠和高效选择.
  • 虽然有希望,但基于物理的神经网络需要进一步发展,以始终匹配或超过FEM性能.
  • 这项研究强调了在数值方法和科学计算的深度学习方面持续研究的必要性.