Abstract
SAND2025-01851O
GP-BayesOpInf is a software tool that uses algorithms to combine Gaussian process regression, principal component analysis, and linear Bayesian inference to produce a probabilistic reduced-order model for time-dependent systems. Numerical examples include the compressible Euler equations for an ideal gas, a heat diffusion process with a nonlinear reaction term, and a set of ordinary differential equations describing a compartmental model in epidemiology. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
- Developers:
-
McQuarrie, Shane [1][2][3] ; Chaudhuri, Anirban [1][2][3][4] ; Willcox, Karen [1][2][3][4] ; Guo, Mengwu [1][2][3][5]
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
- University of Texas at Austin
- Lund University
- Contributors:
-
Other: Chaudhuri, Anirban [1] ; Willcox, Karen [1] ; Guo, Mengwu [2] - University of Texas at Austin
- Lund University
- Contributing Organizations:
-
Other: University of Texas at Austin Other: Lund University
- Release Date:
- 2024-09-12
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Python
- Version:
- 1.0
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:NA0003525
- Code ID:
- 163293
- Site Accession Number:
- SCR #3099.0
- Research Org.:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Country of Origin:
- United States
- Keywords:
- SciDAC
Citation Formats
McQuarrie, Shane, Chaudhuri, Anirban, Willcox, Karen, Guo, Mengwu, Chaudhuri, Anirban, Willcox, Karen, and Guo, Mengwu.
GP-BayesOpInf.
Computer Software.
https://github.com/sandialabs/GP-BayesOpInf.
USDOE.
12 Sep. 2024.
Web.
doi:10.11578/dc.20250909.16.
McQuarrie, Shane, Chaudhuri, Anirban, Willcox, Karen, Guo, Mengwu, Chaudhuri, Anirban, Willcox, Karen, & Guo, Mengwu.
(2024, September 12).
GP-BayesOpInf.
[Computer software].
https://github.com/sandialabs/GP-BayesOpInf.
https://doi.org/10.11578/dc.20250909.16.
McQuarrie, Shane, Chaudhuri, Anirban, Willcox, Karen, Guo, Mengwu, Chaudhuri, Anirban, Willcox, Karen, and Guo, Mengwu.
"GP-BayesOpInf." Computer software.
September 12, 2024.
https://github.com/sandialabs/GP-BayesOpInf.
https://doi.org/10.11578/dc.20250909.16.
@misc{
doecode_163293,
title = {GP-BayesOpInf},
author = {McQuarrie, Shane and Chaudhuri, Anirban and Willcox, Karen and Guo, Mengwu and Chaudhuri, Anirban and Willcox, Karen and Guo, Mengwu},
abstractNote = {SAND2025-01851O
GP-BayesOpInf is a software tool that uses algorithms to combine Gaussian process regression, principal component analysis, and linear Bayesian inference to produce a probabilistic reduced-order model for time-dependent systems. Numerical examples include the compressible Euler equations for an ideal gas, a heat diffusion process with a nonlinear reaction term, and a set of ordinary differential equations describing a compartmental model in epidemiology. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.},
doi = {10.11578/dc.20250909.16},
url = {https://doi.org/10.11578/dc.20250909.16},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20250909.16}},
year = {2024},
month = {sep}
}