  Transformers are state-of-the-art deep learning models that are composed of
stacked attention and point-wise, fully connected layers designed for handling
sequential data. Transformers are not only ubiquitous throughout Natural
Language Processing (NLP), but, recently, they have inspired a new wave of
Computer Vision (CV) applications research. In this work, a Vision Transformer
(ViT) is applied to predict the state variables of 2-dimensional Ising model
simulations. Our experiments show that ViT outperform state-of-the-art
Convolutional Neural Networks (CNN) when using a small number of microstate
images from the Ising model corresponding to various boundary conditions and
temperatures. This work opens the possibility of applying ViT to other
simulations, and raises interesting research directions on how attention maps
can learn about the underlying physics governing different phenomena.
