Documentation

The detailed API documentation for gpCAM can be found here:

gpCAM is built on the upstream library fvGP.  For detailed inquiries, visit that page for information.  

• gpCAM Documentation

• fvGP Documentation

Bayesian Optimization, Gaussian process uncertainty quantification, and  Autonomous experimentation can be complicated endeavors employing user-defined acquisition functions, cost functions, training procedures, and optimization algorithms. Make sure to test your setup a few weeks before a high-stakes application, and please feel free to reach out if you encounter difficulties.

gpCAM ships with a set of Claude Code (compatible with other AI models) skills that guide an AI assistant through designing autonomous experiments — custom kernels, acquisition functions, noise models, and the full ask/tell/train loop. Experimentalists who want smart, autonomous data acquisition without deep knowledge of GP math or the gpCAM API can use these skills to design autonomous experiments.

Download the skill directly from here.

The codes for the three packages can be found here:

• https://github.com/lbl-camera/gpCAM

• https://github.com/lbl-camera/fvGP

• https://github.com/lbl-camera/HGDL

Install all of them as part of the gpCAM package via: pip install gpcam

More installation information can be found here.