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Gaussian Process Regression framework for numerical integration and differentiation

Project description

CalcGP: Numerical Calculus via Gaussian Process Regression

  1. Introduction
  2. Installation


CalcGP is a Gaussian Process Regression framework built as an alternative for numerical integration of gradients and differentiation of scalar functions. The package is based on the autograd framework JAX.

CalcGP is intended to be used as a regression framework for scalar functions and gradients of scalar functions. One can

  • directly fit a scalar function from observations,
  • "integrate" the gradient of a scalar function,
  • "differentiate" a scalar function in order to get its gradient.

Examples for all these cases can be found in ./examples/. There is a 1D example that shows all three use cases, a 2D example on how to handle higher dimensional data, and an example that shows a sparse model for large datasets.


Download the package from github via

git clone

Then, to install the package directly, run

python3 install --user

or to install it in a conda environment run

conda create -n myenv python=3.8
conda activate myenv
python3 install

direcly in the newly created ./calcgp/ folder.

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