  Computational models are quantitative representations of systems. By
analyzing and comparing the outputs of such models, it is possible to gain a
better understanding of the system itself. Though as the complexity of model
outputs increases, it becomes increasingly difficult to compare simulations to
each other. While it is straightforward to only compare a few specific model
outputs across multiple simulations, additional useful information can come
from comparing model simulations as a whole. However, it is difficult to
holistically compare model simulations in an unbiased manner. To address these
limitations, we use representation learning to transform model simulations into
low-dimensional points, with the neural networks capturing the relationships
between the model outputs without the need to manually specify which outputs to
focus on. The distance in low-dimensional space acts as a comparison metric,
reducing the difference between simulations to a single value. We provide an
approach to training neural networks on model simulations and display how the
trained networks can then be used to provide a holistic comparison of model
outputs. This approach can be applied to a wide range of model types, providing
a quantitative method of analyzing the complex outputs of computational models.
