Welcome to CARPoolGP documentation!

CARPoolGP is an sampling and regression technique developed in https://arxiv.org/abs/2403.10609 . The basic idea, is that when we can force correlations between samples in parameter space, we can reduce variance on emulated quantities. CARPoolGP leverages the CARPool method of https://arxiv.org/abs/2009.08970 and Gaussian process regression.

CARPoolGP can be used:

1. To emulate a quantity throughout some parameter space given preexisting samples

2. Learn the best place in parameter space to generate new samples at (Active Learning)

We provide here a tutorial with a one dimensional toy example, an application using simulations from GZ here, and a an application to emulate profiles again using the simulations of:

If using in your own work, please Citation our work!

Note

This project is under active development.

Contents

See the Installing CARPoolGP section for details on getting started with CARPoolGP. To find a brief description of the theoretical framework for CARPoolGP see CARPoolGP Theory. We include tutorials in the Tutorials section.

• Installing CARPoolGP
• CARPoolGP Theory
• Tutorials
• Contact