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 Order systems II01:18

Second Order systems II

73
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.
73
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

34
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...
34
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

80
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....
80
Second-order Op Amp Circuits01:19

Second-order Op Amp Circuits

211
Implementing second-order low-pass filters in audio systems is crucial in refining audio signals by eliminating undesirable high-frequency noise. These filters typically involve second-order op-amp circuits configured as voltage followers, encompassing two nodes with distinct storage elements.
The analysis of such circuits follows a systematic approach, similar to the second-order RLC circuits. In practical scenarios, bulky inductors are rarely employed due to their size and weight. This means...
211
Second Order systems I01:20

Second Order systems I

120
A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
120
First Order Systems01:21

First Order Systems

79
First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
79

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

FGFR1-ETV1-CXCL1 signaling in dermal fibroblast orchestrates fibroblast-associated itch.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

N<sup>6</sup>-methyladenine modification of DNA enhances RecA-mediated homologous recombination.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Performance of the Risk Scores for Predicting In-Hospital Mortality in Patients with Acute Coronary Syndrome in a Chinese Cohort.

Reviews in cardiovascular medicine·2024
Same author

Total Arterial Revascularization in Diabetic Patients Undergoing Coronary Artery Bypass Graft Surgery: A Systematic Review and Meta-Analysis.

Reviews in cardiovascular medicine·2024
Same author

Patient-Specific Factors Predicting Renal Denervation Response in Patients With Hypertension: A Systematic Review and Meta-Analysis.

Journal of the American Heart Association·2024
Same author

Heterogeneous body compositions and all-cause mortality in acute coronary syndrome patients: a ten-year retrospective cohort study.

Journal of geriatric cardiology : JGC·2024
Same journal

A practical design of backdoor trigger under frequency-based orthogonality constraints.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

EEG fine-grained visual semantic decoding via a multimodal framework.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Collaborative-adversarial jailbreaking: A propagation-aware attack framework for multi-agent code generation systems.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Theoretical analysis of the denoising autoencoder using Tweedie's formula.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Frequency-based cross-attention fusion network for RGB-D salient object detection.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

HTNet: A self-supervised heterogeneous triple network for multi-modal data.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: May 16, 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

对于结构动态响应预测的FE减少顺序模型信息的神经操作员.

Lai-Hao Yang1, Xu-Liang Luo1, Zhi-Bo Yang2

  • 1School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, PR China.

Neural networks : the official journal of the International Neural Network Society
|April 2, 2025
PubMed
概括
此摘要是机器生成的。

基于物理学的神经网络 (PINN) 与结构动态作斗争. 一种基于里埃神经运算子 (FNO) 的新方法FRINO提供了卓越的准确性和速度,可以在各种激发下预测结构反应.

关键词:
数据驱动的,基于物理的神经网络 (PINN)里埃神经运算子 (FNO) 是一个神经运算子.减少订单的模型 (ROM)结构动力学 结构动力学

更多相关视频

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.1K
Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants
06:59

Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants

Published on: March 1, 2019

7.6K

相关实验视频

Last Updated: May 16, 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
Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.1K
Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants
06:59

Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants

Published on: March 1, 2019

7.6K

科学领域:

  • 结构动力学 结构动力学
  • 计算力学 计算力学 计算力学
  • 机器学习 机器学习

背景情况:

  • 基于物理学的神经网络 (PINN) 对微分方程有希望,但在结构动态中面临着准确性和效率的挑战.
  • 直接嵌入大型结构模型作为神经网络中的约束,妨碍了可训练性和精度.

研究的目的:

  • 引入一种基于福里埃神经运算子 (FNO) 的新方法FRINO,用于高精度,低成本和多功能结构动态响应预测.
  • 克服PINNs在处理复杂结构动态模型方面的局限性.

主要方法:

  • 采用福里埃神经运算符 (FNO) 来捕获结构动态的频域特征.
  • 通过正确的直角分解集成了减少顺序模型 (ROM),以执行物理约束并降低计算成本.
  • 验证了FRINO方法,使用悬臂光束在各种激发下进行动态响应预测.

主要成果:

  • FRINO准确地预测结构动态反应和固有的动态特征.
  • 实现了比PINNs高出两个数量级的预测准确度.
  • 与PINNs相比,已经证明了高达3个数量级的计算速度提升.
  • 在各种未知刺激下,FRINO 显示出广泛的灵活性来预测各种未知刺激下的反应.

结论:

  • 在结构动态响应预测方面,FRINO比PINNs有显著的进步.
  • 该方法提供了高精度,计算效率和多功能性.
  • 最佳的FRINO性能需要仔细考虑物理损失,数据分辨率和网络架构.