Siamese neural networks for a generalized, quantitative comparison of complex model outputs
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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, it is more informative to be able to compare model simulations as a whole. However, it is difficult to holistically compare model simulations in an unbiased manner. To address these limitations, we use Siamese neural networks to compare model simulations as a single value, with the neural networks capturing the relationships between the model outputs. We provide an approach to training Siamese 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.
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