Kriging Toolkit for Python

GSTools - A geostatistical toolbox: random fields, variogram estimation, covariance models, kriging and much more

Kriging | Poisson Kriging | Variogram Analysis

Geostatistics in Python

In this repository I publish the python code, that was part of my master thesis. The thesis can be found here, however its in German though, sry. :/

Python tool for creating Kriging surrogate models

The repo is the official implementation for the paper: MoGERNN: An Inductive Traffic Predictor for Unobserved Locations.

Implementation of image reparation and inpainting using Gaussian Conditional Simulation. Created as part of Unity Technologies research.

Spatial interpolation python package

Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference

"Space-Time Interpolation and Forecasting" - Predicting spatio-temporally distributed variables via space-time regression kriging using numpy and numba.

GeoKrige is a Python package designed for spatial interpolation using Kriging Methods. While primarily tailored for geospatial analysis, it is equally applicable to other spatial analysis tasks.

Laplacian-enhanced tensor learning for large-scale spatiotemporal traffic data kriging (estimation)

An empirical study and comparison of Deterministic, Statistical, and ML Algorithms for the Spatial Modeling of significant wave height data from NOAA's National Data Buoy Center and other Environment-related datasets

POD+K in Real-Time Aeroelastic Pre-design Problem

Spatial interpolations using Kriging (Ordinary, Simple and External Drift) and IDW. Variogram fitting and clustering. Extraction of time series from raster data (GTiff and NC) to HDF5 or text. Take a look at the scripts in the "test" directory for how to use.

Highly performant and scalable out-of-the-box gaussian process regression and Bernoulli classification. Built upon GPyTorch, with a familiar sklearn api.

Spatiotemporal Gaussian process modeling for environmental data: non-stationary PDE prior, deep kernels, multi-fidelity fusion, and A-optimal sampling.非稳态 PDE + 核深度学习 + 多保真 Co-Kriging + 主动采样的物理约束克里金方法，用于复杂时空环境建模与预测

Repository for Variogram, Correlation and Kriging Estimation (VarioCorreKrigE)