A Stochastic Locally Diffusive Model with Neural Network-Based Deformations for Global Sea Surface Temperature
Peer reviewed, Journal article
Accepted version
Åpne
Permanent lenke
https://hdl.handle.net/11250/2984296Utgivelsesdato
2021Metadata
Vis full innførselSamlinger
- Institutt for matematiske fag [2352]
- Publikasjoner fra CRIStin - NTNU [37219]
Originalversjon
10.1002/sta4.431Sammendrag
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.