4 citations · 8 across the 8 of their papers we have counts for
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Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case Study
Arnab Hazra, Pratik Nag, Rishikesh Yadav +1
Increasingly large and complex spatial datasets pose massive inferential challenges due to high computational and storage costs. Our study is motivated by the KAUST Competition on…
Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields
Pratik Nag, Ying Sun, Brian J Reich
High spatial resolution wind data are essential for a wide range of applications in climate, oceanographic and meteorological studies. Large-scale spatial interpolation or downscal…
Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural Networks
Pratik Nag, Yiping Hong, Sameh Abdulah +3
Spatial processes observed in various fields, such as climate and environmental science, often occur on a large scale and demonstrate spatial nonstationarity. Fitting a Gaussian pr…
Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecasting
Pratik Nag, Ying Sun, Brian J Reich
Gaussian processes (GP) and Kriging are widely used in traditional spatio-temporal mod-elling and prediction. These techniques typically presuppose that the data are observed from…