Rapid measurements and phase transition detections made simple by AC-GANs
Jiewei Ding, Ho-Kin Tang, Wing Chi Yu
SciPost Phys. Core 7, 035 (2024) · published 11 June 2024
- doi: 10.21468/SciPostPhysCore.7.2.035
- Submissions/Reports
Abstract
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.
Authors / Affiliations: mappings to Contributors and Organizations
See all Organizations.- 1 Jiewei Ding,
- 2 Ho-Kin Tang,
- 1 Wing Chi Yu
- City University of Hong Kong
- National Natural Science Foundation of China [NSFC]
- Research Grants Council, University Grants Committee (through Organization: University Grants Committee [UGC])
- 哈尔滨工业大学 / Harbin Institute of Technology [HIT]