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Rapid Measurements and Phase Transition Detections Made Simple by AC-GANs
by Jiewei Ding, Ho-Kin Tang, Wing Chi Yu
This Submission thread is now published as
Submission summary
| Authors (as registered SciPost users): | Wing Chi Yu |
| Submission information | |
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| Preprint Link: | scipost_202401_00034v3 (pdf) |
| Date accepted: | 2024-05-20 |
| Date submitted: | 2024-05-10 09:41 |
| Submitted by: | Yu, Wing Chi |
| Submitted to: | SciPost Physics Core |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approach: | Computational |
Abstract
In recent years, the use of end-to-end neural networks to analyze Monte Carlo data has received a lot of attention. However, the application of non-end-to-end generative ad- versarial neural networks is less explored. Here, we study classical many-body systems using generative adversarial neural networks. We use the conditional generative adversarial network with an auxiliary classifier (AC-GAN) and introduce self-attention layers into the generator, enabling the model to learn the distribution of two-dimensional XY model spin configurations as well as the physical quantities. By applying the symmetry of the systems, we further find that AC-GAN can be trained with a very small raw dataset, allowing us to obtain reliable measurements in the model that requires a large sample size, e.g. the large-sized 2D XY model and the 3D constrained Heisenberg model. We also find that it is possible to quantify the distribution changes that occur in the configuration of the models during phase transitions and locate the phase transition points by AC-GAN.
List of changes
1. Typeset the captions in Figure 4 and Figure 5.
2. Added DOI to the references.
3. Clarified some statements to improve the presentation.
4. Corrected typos and grammatical errors.
Published as SciPost Phys. Core 7, 035 (2024)