Autonomous Data Acquisition, Uncertainty Quantification and HPC Optimization
gpCAM is powered by
The Center for Advanced Mathematics for Energy Research Applications
Lawrence Berkeley National Laboratory
Mission of the project
gpCAM is primarily a project concerned with HPC stochastic function approximation, optimization, and autonomous experimentation. It is also an API and software designed to make autonomous data acquisition and analysis for experiments and simulations faster, simpler, and more widely available. The tool is based on a flexible and powerful Gaussian process regression at the core. The flexibility stems from the modular design of gpCAM which allows the user to implement and import their own Python functions to customize and control almost every aspect of the software. That makes it possible to easily tune the algorithm to account for various kinds of physics and other domain knowledge and to identify and find interesting features and functional characteristics. A specialized function optimizer in gpCAM can take advantage of HPC architectures for fast analysis time and reactive autonomous data acquisition.
The core Gaussian-Process engine is available as a standalone package called "fvGP" which is being developed as part of this project. Gaussian processes and autonomous experimentation are both highly dependent on function optimization. To satisfy the need for high-performance optimization, we are developing the package "HGDL" as part of this project.
The API is designed in a way that makes it easy to be used and allows endless customization.
gpCAM is implemented using torch and DASK for fast training and predictions on distributed compute architecture
Contact MarcusNoack@lbl.gov to get more information on the project. We also encourage you to join the SLACK channel.
Supported by the US Department of Energy Office of Science
Advanced Scientific Computing Research (email@example.com)
Basic Energy Sciences (Peter.Lee@science.doe.gov)