  Photoelastic techniques have a long tradition in both qualitative and
quantitative analysis of the stresses in granular materials. Over the last two
decades, computational methods for reconstructing forces between particles from
their photoelastic response have been developed by many different experimental
teams. Unfortunately, all of these methods are computationally expensive. This
limits their use for processing extensive data sets that capture the time
evolution of granular ensembles consisting of a large number of particles. In
this paper, we present a novel approach to this problem which leverages the
power of convolutional neural networks to recognize complex spatial patterns.
The main drawback of using neural networks is that training them usually
requires a large labeled data set which is hard to obtain experimentally. We
show that this problem can be successfully circumvented by pretraining the
networks on a large synthetic data set and then fine-tuning them on much
smaller experimental data sets. Due to our current lack of experimental data,
we demonstrate the potential of our method by changing the size of the
considered particles which alters the exhibited photoelastic patterns more than
typical experimental errors.
