SURF is used to study the spatial variability and hidden patterns in large datasets. Initial alpha versions were implemented for spatial uncertainty quantification.
SURF creates surrogate models that assist users in making predictions. This library features both classical random field models and machine-learning algorithms as the backends for spatial uncertainty quantification. It has been incorporated in SimCenter efforts such as the hurricane and earthquake testbeds for uncertainty quantification and data enhancement.