TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysCore.7.2.035
TI  - Rapid measurements and phase transition detections made simple by AC-GANs
PY  - 2024/06/11
UR  - https://scipost.org/SciPostPhysCore.7.2.035
JF  - SciPost Physics Core
JA  - SciPost Phys. Core
VL  - 7
IS  - 2
SP  - 035
A1  - Ding, Jiewei
AU  - Tang, Ho-Kin
AU  - Yu, Wing Chi
AB  - In recent years, significant attention has been paid to using end-to-end neural networks for analyzing Monte Carlo data. However, the exploration of non-end-to-end generative adversarial neural networks remains limited. Here, we investigate classical many-body systems using generative adversarial neural networks. We employ the conditional generative adversarial network with an auxiliary classifier (AC-GAN) and integrate self-attention layers into the generator. This modification enables the network learn the distribution of the two-dimensional (2D) XY model's spin configurations and the physical quantities of interest. Utilizing the symmetry of the systems, we discover that AC-GAN can be trained with a very small raw dataset. This approach allows us to obtain reliable measurements for models typically demanding large samples, such as the large-sized 2D XY and the 3D constrained Heisenberg models. Moreover, we demonstrate the capability of AC-GAN to identify the phase transition points by quantifying the distribution changes in the spin configurations of the systems.
ER  -

@Article{10.21468/SciPostPhysCore.7.2.035,
	title={{Rapid measurements and phase transition detections made simple by AC-GANs}},
	author={Jiewei Ding and Ho-Kin Tang and Wing Chi Yu},
	journal={SciPost Phys. Core},
	volume={7},
	pages={035},
	year={2024},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCore.7.2.035},
	url={https://scipost.org/10.21468/SciPostPhysCore.7.2.035},
}