KenjiHarada / FSS-tools

The finite-size scaling methods for critical phenomena

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FSS-tools

The finite-size scaling (FSS) method is a powerful tool for getting universal information of critical phenomena. It estimates universal information from observables of critical phenomena at finite-size systems.

Here, we introduce an implementation with PyTorch of two FSS methods by using Gaussian process (GP) and a neural network (NN).

Install

We prepare the fss_torch module to apply a FSS method by GP or NN in src/fss_torch.

Prerequisites

The module PyTorch and GPyTorch are required.

Quickstart

To use our FSS methods by GP and NN, see the following jupyter notebooks in examples.

Related sites

Citations

  1. Kenji Harada: Bayesian inference in the scaling analysis of critical phenomena, Physical Review E 84 (2011) 056704.
    DOI: 10.1103/PhysRevE.84.056704
  2. Kenji Harada: Kernel method for corrections to scaling, Physical Review E 92 (2015) 012106.
    DOI: 10.1103/PhysRevE.92.012106
  3. Ryosuke Yoneda and Kenji Harada : Neural Network Approach to Scaling Analysis of Critical Phenomena, arXiv:2209.01777.

History

  • March 14, 2022: The first release (v0.1.0)

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The finite-size scaling methods for critical phenomena

License:MIT License


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Language:Python 100.0%