Representation learning for a generalized, quantitative comparison of complex model outputs
This provides a quantitative method for researchers and practitioners to compare complex model simulations without manual specification, though it is incremental as it builds on existing representation learning techniques.
The paper tackles the problem of comparing complex computational model outputs by using representation learning to transform simulations into low-dimensional points, with distances serving as a single comparison metric, enabling holistic and unbiased analysis across various model types.
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.