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Found 3 papers, 1 papers with code
Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields
Large-scale spatial interpolation or downscaling of bivariate wind fields having velocity in two dimensions is a challenging task because wind data tend to be non-Gaussian with high spatial variability and heterogeneity.
Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural Networks
Fitting a Gaussian process with a nonstationary Mat\'ern covariance is challenging.
Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecasting
Gaussian processes (GP) and Kriging are widely used in traditional spatio-temporal mod-elling and prediction.
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