4 citations · 8 across the 8 of their papers we have counts for
8 papers
Spatio-temporal modeling and forecasting with Fourier neural operators
Pratik Nag, Andrew Zammit-Mangion, Sumeetpal Singh +1
Spatio-temporal process models are often used for modeling dynamic physical and biological phenomena that evolve across space and time. These phenomena may exhibit environmental he…
Deep classifier kriging for probabilistic spatial prediction of air quality index
Junyu Chen, Pratik Nag, Huixia Judy-Wang +1
Accurate spatial interpolation of the air quality index (AQI), computed from concentrations of multiple air pollutants, is essential for regulatory decision-making, yet AQI fields…
Modeling nonstationary spatial processes with normalizing flows
Pratik Nag, Andrew Zammit-Mangion, Ying Sun
Nonstationary spatial processes can often be represented as stationary processes on a warped spatial domain. Selecting an appropriate spatial warping function for a given applicati…
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…
The Second Competition on Spatial Statistics for Large Datasets
Sameh Abdulah, Faten Alamri, Pratik Nag +4
In the last few decades, the size of spatial and spatio-temporal datasets in many research areas has rapidly increased with the development of data collection technologies. As a re…