Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Network Function of a Circuit01:25

Network Function of a Circuit

254
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
254
Relation between Mathematical Equations and Block Diagrams01:20

Relation between Mathematical Equations and Block Diagrams

162
In a spring-mass-damper system, the second-order differential equation describes the dynamic behavior of the system. When transformed into the Laplace domain under zero initial conditions, this equation can be effectively analyzed and manipulated. The transformation into the Laplace domain converts differential equations into algebraic equations, simplifying the process of isolating the output.
162
Transient and Steady-state Response01:24

Transient and Steady-state Response

138
In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state...
138
Classification of Systems-I01:26

Classification of Systems-I

167
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
167
Circuit Terminology01:14

Circuit Terminology

598
An electrical network is a system composed of interconnected elements, such as resistors, capacitors, inductors, and voltage or current sources. Unlike a circuit, an electrical network does not necessarily form a closed path. In other words, while all circuits can be considered networks due to their interconnected nature, not every network qualifies as a circuit.
A circuit, on the other hand, is also an interconnected system of electrical elements but must contain one or more closed paths.
598
Signal and System01:26

Signal and System

612
A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
612

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Alkali Cations Mediated Subnanochannels in MXene Membranes for Enhanced Selective Ion Transport.

Angewandte Chemie (International ed. in English)·2026
Same author

Surface-Modified InVGr as a Thermal Interface Material with High Thermal Conductivity and Low Contact Thermal Resistance.

ACS applied materials & interfaces·2026
Same author

Microalgae-Based Semiartificial Photosynthesis: Strategies, Applications, and Future Prospects.

Environmental science & technology·2026
Same author

Preparation of Alginate Oligosaccharides by Autoclaving Pretreatment Combined with Enzymatic Method.

Marine drugs·2026
Same author

Degradation and utilization of chondroitin sulfate with different molecular weights by the gut microbiota from four midlife women: Role of Bacteroides ovatus AP1.

Carbohydrate polymers·2026
Same author

Preparation and Identification of the Novel Umami Peptides from Sea Cucumber Viscera Hydrolysate.

Foods (Basel, Switzerland)·2026

Related Experiment Video

Updated: May 29, 2025

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

Accurately Models the Relationship Between Physical Response and Structure Using Kolmogorov-Arnold Network.

Yang Wang1, Changliang Zhu2, Shuzhe Zhang3

  • 1State Key Laboratory for Mechanical Behavior of Materials, School of Materials Science and Engineering, Xi'an Jiaotong University, Xi'an, 710049, P. R. China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|February 8, 2025
PubMed
Summary
This summary is machine-generated.

This study uses the Kolmogorov-Arnold Network (KAN) to predict Poisson's ratio transitions in hexagonal lattices. KAN clarifies the link between geometric properties and the shift from positive to negative Poisson's ratios.

Keywords:
Deep learningKolmogorov‐Arnold NetworkMechanical propertyPoisson's ratio

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

979
Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.0K

Related Experiment Videos

Last Updated: May 29, 2025

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
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

979
Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.0K

Area of Science:

  • * Physics and Materials Science
  • * Computational Science and Artificial Intelligence

Background:

  • * Current machine learning methods in science often lack interpretability, hindering understanding of underlying physical mechanisms.
  • * Understanding phenomena like Poisson's ratio in elastic networks is crucial for scientific progress.

Purpose of the Study:

  • * To investigate the Poisson's ratio of a hexagonal lattice elastic network during structural deformation.
  • * To utilize the Kolmogorov-Arnold Network (KAN) for interpretable AI-driven analysis of physical phenomena.
  • * To elucidate the mathematical relationship between geometric properties and Poisson's ratio transitions.

Main Methods:

  • * Application of the Kolmogorov-Arnold Network (KAN) to model a hexagonal lattice elastic network.
  • * Analysis of structural deformation and its effect on the network's Poisson's ratio.
  • * Mathematical framework development using KAN to describe the transition from positive to negative Poisson's ratio.

Main Results:

  • * Accurate prediction of the Poisson's ratio transition from positive to negative.
  • * Identification of the critical geometric parameters where Poisson's ratio equals zero.
  • * Demonstration of KAN's ability to reveal the connection between geometry and material behavior.

Conclusions:

  • * The Kolmogorov-Arnold Network (KAN) offers an interpretable approach to understanding complex physical behaviors.
  • * This study highlights KAN's potential in clarifying mathematical relationships governing structural responses.
  • * KAN facilitates a deeper understanding of how geometric changes influence material properties like Poisson's ratio.