  Many nonequilibrium systems, such as biochemical reactions and socioeconomic
interactions, can be described by reaction-diffusion equations that demonstrate
a wide variety of complex spatiotemporal patterns. The diversity of the
morphology of these patterns makes it difficult to classify them quantitatively
and they are often described visually. Hence, searching through a large
parameter space for patterns is a tedious manual task. We discuss how
convolutional neural networks can be used to scan the parameter space,
investigate existing patterns in more detail, and aid in finding new groups of
patterns. As an example, we consider the Gray-Scott model for which training
data is easy to obtain. Due to the popularity of machine learning in many
scientific fields, well maintained open source toolkits are available that make
it easy to implement the methods we discuss in advanced undergraduate and
graduate computational physics projects.
