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Creators/Authors contains: "Hu, Wenjing"

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  1. In this work, we propose a new approach to model large, irregularly distributed spatio‐temporal global data via a locally diffusive stochastic partial differential equation (SPDE). The proposed model assumes a local deformation of the SPDE with non‐linear dependence on the covariates through a neural network. The proposed model can be fit in a computationally efficient manner using a triangulation over the sphere and sparsity of the precision matrix, as shown in an application with a large data set of simulated multi‐decadal monthly sea surface temperature. 
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