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Updated: May 21, 2025

In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging

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Published on: September 2, 2016

Diffusion reconstruction for the diluted Ising model.

Stefano Bae¹, Enzo Marinari^1,2, Federico Ricci-Tersenghi^1,2

¹Sapienza Università di Roma, Dipartimento di Fisica, P. A. Moro 5, 00185 Roma, Italy.

Physical Review. E

|March 19, 2025

Summary

This summary is machine-generated.

Diffusion models can now generate high-quality physical datasets. Researchers used a Landau-Ginzburg-like model to accurately reproduce statistical properties of the Ising model.

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Area of Science:

Physics
Machine Learning
Computational Physics

Background:

Diffusion models excel at generating multimedia data.
Their application to generating physical model datasets is unexplored.
Understanding physical systems requires high-quality data.

Purpose of the Study:

To investigate the efficacy of diffusion models in generating physical datasets.
To apply a Landau-Ginzburg-like diffusion model to a complex physical system.
To assess if generated data captures essential physical properties.

Main Methods:

Utilized a Landau-Ginzburg-like diffusion model.
Applied the model to infer the distribution of a 2D bond-diluted Ising model.
Analyzed generated samples for statistical and critical property reproduction.

Main Results:

The diffusion model successfully inferred the data distribution.
Generated samples accurately reproduced key statistical properties.
Critical properties of the physical model were correctly captured.

Conclusions:

Diffusion models are effective for generating high-quality physical datasets.
This approach offers a novel method for simulating physical systems.
The study validates the use of diffusion models beyond multimedia generation.