fvGP

PyPI Documentation Status fvGP CI Codecov PyPI - License DOI

Python package for highly flexible function-valued Gaussian processes (fvGP)

It is recommended to use this package via gpCAM.

Specialties: Extreme-Scale GPs, GPs Tailored for HPC training, Advanced Kernel Designs, Domain-Aware Stochastic Function Approximation

Coming soon: All those capabilities for stochastic manifold learning

fvGP holds the world record for exact large-scale Gaussian Processes!

Credits

This code was developed with help from Ron Pandolfi (LBNL), Mark Risser (LBNL), Hengrui Luo (Rice U.), and Vardaan Tekriwal (UCB).

Additional contributions and insights came from across the community, in particular, Kevin Yager, Masafumi Fukuto, and their teams (Brookhaven National Lab).

We acknowledge support from several DOE ASCR, BER, and BES projects, including CAMERA (James Sethian), SPECTRA (Sherry Li), and CASCADE (Bill Collins), as well as support directly from Lawrence Berkeley National Laboratory.

This package uses the HGDL package of David Perryman and Marcus Noack, which is based on the HGDN algorithm by Noack and Funke.

Project links

Key dates

PyPI data
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  • Released:
Newer release available (4.8.8)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for MarcusMichaelNoack from gravatar.com MarcusMichaelNoack Avatar for ronpandolfi from gravatar.com ronpandolfi

Credits

Author/Maintainer: Marcus Michael Noack

License

GNU General Public License v3 or later (GPLv3+) (*** License Agreement ***)

Requires

Python >=3.10

Provides Extra

docs plotting preconditioners tests

Tags

adaptive autonomous gui qt self driving

Classifiers

Report project as malware

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