Kriging Toolkit for Python
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Apr 14, 2021 - Python
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kriging
Here are 64 public repositories matching this topic...
GSTools - A geostatistical toolbox: random fields, variogram estimation, covariance models, kriging and much more
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May 7, 2021 - Python
Fast, memory-efficient 3D spline interpolation and global kriging, via RBF (radial basis function) interpolation.
cpp
interpolation
regression
approximation
surface-reconstruction
smoothing
splines
spline
geostatistics
kriging
function-approximation
radial-basis-function
rbf
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Oct 23, 2020 - C++
Geostatistics in Python
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Jul 26, 2020 - Python
Package with spatial analysis and spatial prediction tools
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May 15, 2021 - Python
This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).
machine-learning
matlab
thompson-sampling
multi-objective-optimization
genetic-algorithms
black-box-optimization
gaussian-processes
bayesian-optimization
kriging
expensive-to-evaluate-functions
surrogate-based-optimization
spectral-sampling
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Jun 19, 2020 - MATLAB
The standard and parallel multiobjective EGO algorithms
constrained-optimization
multi-objective-optimization
bayesian-optimization
kriging
expected-improvement
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Aug 13, 2020 - MATLAB
Repository of the GammaRay geomodeling software. New in April, 12th: export GeoGrid as Eclipse grid.
simulation
random-forest
regression
estimation
classification
fft
cart
svd
gabor
markov-chain-monte-carlo
geostatistics
gslib
earthsciences
kriging
variogram
emd
markov-random-field
discrete-wavelet-transformation
stratigraphic-grid
vertical-proportion-curve
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May 6, 2021 - C++
The STK is a (not so) Small Toolbox for Kriging. Its primary focus is on the interpolation/regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior.
machine-learning
gaussian-processes
geostatistics
kriging
gaussian-process-regression
gaussian-process-optimisation
computer-experiment-design
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Mar 28, 2021 - MATLAB
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
construction
thesis
polynomials
kriging
radial-basis-function
wing
structural-analysis
surrogate
latin-hypercube
aircraft-design
openmdao
surrogate-models
halton
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Aug 17, 2019 - Python
Gaussian process regression
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Apr 21, 2021 - Julia
Software package for Gaussian Process (GP) modelling written in R language. The core functions are coded in C++ and based on the EIGEN library (through RcppEigen).
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Aug 10, 2018 - R
Generate stocastic Gaussian realization constrained to a coarse scale image.
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Sep 9, 2019 - HTML
Mandatory work for Introduction to Geostatistics course on University of Buenos Aires (UBA)
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Dec 12, 2016 - R
A MATLAB implementation of the ParEGO algorithm
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Aug 13, 2020 - MATLAB
Kriging estimators for the GeoStats.jl framework
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May 15, 2021 - Julia
Multifidelity Kriging, Efficient Global Optimization
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Nov 20, 2018 - Jupyter Notebook
Kriging
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Feb 11, 2018 - PostScript
Data fusion for air quality data
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Nov 17, 2016 - HTML
Implementation of image reparation and inpainting using Gaussian Conditional Simulation. Created as part of Unity Technologies research.
innovation
repair
kriging
inpainting
adsn
conditional-simulation
gaussian-textures
2d-inpainter
image-reparation
texture-inpainting
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Jun 24, 2020 - Python
Robust Simulation Optimization Kriging
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Feb 6, 2020 - MATLAB
Autonomous field exploration simulation framework
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Dec 12, 2018 - MATLAB
A demo of how to use a surrogate model for an optimization problem
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Apr 3, 2021 - Jupyter Notebook
Global surface temperature layers are interpolated based on a point measurement data set of the worldwide surface temperature, which has been recorded since 1950. For the spatial interpolation, an universal Kriging approach is applied with additional layers for the continentality, the atmospheric distance, the North-South topographic gradient and the sun inclination angle of every pixel.
interpolation
spatial-analysis
spatial-data
climate-change
kriging
spatial-statistics
universal-kriging
global-temperature
semivariogram
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Apr 19, 2020 - R
Repository for my talk at Python Conference ID 2020
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Jan 25, 2021 - Jupyter Notebook
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The format of module is not completely compatible to sklearn. The reason for adopting to sklearn is because of the parameter optimization. Sklearn provides well-defined tool for tuning parameter and validation. Thus, making the module to adopt sklearn is kind of way to simplify future work.