  The deep learning framework is witnessing expansive growth into diverse
applications such as biological systems, human cognition, robotics, and the
social sciences, thanks to its immense ability to extract essential features
from complicated systems. In particular, recent developments of the field have
revealed the unique faculty of deep learning to accurately approximate complex
physical systems in fluid dynamics, condensed matter physics, etc. The
convolutional neural network (CNN) is an efficient approach to represent
complex systems with large degrees of freedom. On the other hand, the
generative adversarial network (GAN), as an unsupervised learning algorithm, is
capable of efficiently imitating the distribution of training data. Here we
leverage this unique property of GAN, in conjunction with CNN methodology, to
establish an Ising simulator whose generator can produce Ising states given
temperature T around the criticality. The generated Ising states well resemble,
and essentially replicate, the data from conventional Monte Carlo simulations.
Our results demonstrate the universality of GAN as a promising tool in the
field of computational and statistical physics.
