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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

592
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
592
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

1.0K
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
1.0K
Singularity Functions for Shear01:26

Singularity Functions for Shear

546
In structural analysis, singularity functions are crucial in simplifying the representation of shear forces in beams under discontinuous loading. These functions describe discontinuous variations in shear force across a beam with varying loads by using a single mathematical expression, regardless of the complexity of the loading conditions. The singularity functions are derived from creating a free-body diagram of the beam and then making conceptual cuts at specific points to examine the shear...
546
Modeling with Differential Equations01:25

Modeling with Differential Equations

334
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
334
Current Growth And Decay In RL Circuits01:30

Current Growth And Decay In RL Circuits

4.2K
The current growth and decay in RL circuits can be understood by considering a series RL circuit consisting of a resistor, an inductor, a constant source of emf, and two switches. When the first switch is closed, the circuit is equivalent to a single-loop circuit consisting of a resistor and an inductor connected to a source of emf. In this case, the source of emf produces a current in the circuit. If there were no self-inductance in the circuit, the current would rise immediately to a steady...
4.2K
Resultant of a General Distributed Loading01:13

Resultant of a General Distributed Loading

1.2K
While designing structures exposed to non-uniform loads, it is crucial to consider the resultant force and its location. This resultant force is a single vector representing the net force applied due to the distributed load.
Examples such as load distribution due to wind and load distribution on a bridge illustrate how this concept is used to analyze and design safe, reliable structures under variable loading conditions. Most structures, such as residential buildings, bridges, and towers, are...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Supervised and Unsupervised Learning with Numerical Computation for the Wolfram Cellular Automata.

Entropy (Basel, Switzerland)·2025
Same author

Supervised, semisupervised, and unsupervised learning of the Domany-Kinzel model.

Physical review. E·2024
Same author

[Cytokines secretion by peripheral blood mononuclear cells from hepatitis C patients after stimulation with synthetic peptides at the highly variable region].

Zhonghua shi yan he lin chuang bing du xue za zhi = Zhonghua shiyan he linchuang bingduxue zazhi = Chinese journal of experimental and clinical virology·2005
Same author

Archaeal proteasomes and other regulatory proteases.

Current opinion in microbiology·2005
Same author

Effect of carbamate esters on neurite outgrowth in differentiating human SK-N-SH neuroblastoma cells.

Chemico-biological interactions·2005
Same author

Resolving overlapped spectra with curve fitting.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2005

Related Experiment Video

Updated: May 5, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

1.7K

Generating the Critical Ising Model via SRGAN: A Schramm-Loewner Evolution Analysis from a Geometric Deep Learning

Yuxiang Yang1, Wei Li1,2, Yanyang Wang1

  • 1Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics, Central China Normal University, Wuhan 430079, China.

Entropy (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

We developed a novel deep learning method to generate large-scale Ising model configurations, preserving critical dynamics and conformal invariance. This physics-informed approach successfully approximates inverse coarse-graining for complex systems.

Keywords:
SRGANSchramm-Lowner evolutioncritical ising model

Related Experiment Videos

Last Updated: May 5, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

1.7K

Area of Science:

  • Statistical Physics
  • Machine Learning
  • Computational Physics

Background:

  • Macroscopic interfaces in 2D critical Ising models follow Schramm-Loewner Evolution (SLE) theory.
  • Inverse coarse-graining is crucial for generating large-scale physical system configurations.

Purpose of the Study:

  • To propose a physics-driven generative approach for inverse coarse-graining.
  • To leverage Geometric Deep Learning (GDL) and Super-Resolution Generative Adversarial Networks (SRGANs) for generating large Ising model configurations.

Main Methods:

  • Utilized SRGANs for inverse coarse-graining.
  • Employed Convolutional Neural Networks (CNNs) with geometric priors (translational and rotational symmetries).
  • Applied an L1-based loss function to maintain domain wall sharpness for discrete spins.

Main Results:

  • Generated large-scale (2048 × 2048) Ising model configurations while preserving physical conservation.
  • Demonstrated that the model generalizes from small to large scales due to inductive bias.
  • SLE analysis and correlation functions confirmed reproduction of critical dynamics and conformal invariance.

Conclusions:

  • The SRGAN-based framework successfully serves as a physics-preserving inverse coarse-graining transformation.
  • This approach effectively encodes universal physical laws of the Ising Hamiltonian.
  • The method validates the application of GDL in simulating critical phenomena.