GP+: A Python library for kernel-based learning via Gaussian processes

In this paper we introduce GP+, an open-source library for kernel-based learning via Gaussian processes (GPs) which are powerful statistical models that are completely characterized by their parametric covariance and mean functions. GP+ is built on PyTorch and provides a user-friendly and object-ori...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Advances in engineering software (1992) Ročník 195; s. 103686
Hlavní autori:	Yousefpour, Amin, Foumani, Zahra Zanjani, Shishehbor, Mehdi, Mora, Carlos, Bostanabad, Ramin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.09.2024
Predmet:	Bayesian optimization Gaussian processes Kernel methods Manifold learning Python library Uncertainty quantification Uncertainty quantification Kernel methods Manifold learning Bayesian optimization Python library Gaussian processes
ISSN:	0965-9978
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	In this paper we introduce GP+, an open-source library for kernel-based learning via Gaussian processes (GPs) which are powerful statistical models that are completely characterized by their parametric covariance and mean functions. GP+ is built on PyTorch and provides a user-friendly and object-oriented tool for probabilistic learning and inference. As we demonstrate with a host of examples, GP+ has a few unique advantages over other GP modeling libraries. We achieve these advantages primarily by integrating nonlinear manifold learning techniques with GPs’ covariance and mean functions. As part of introducing GP+, in this paper we also make methodological contributions that (1) enable probabilistic data fusion and inverse parameter estimation, and (2) equip GPs with parsimonious parametric mean functions which span mixed feature spaces that have both categorical and quantitative variables. We demonstrate the impact of these contributions in the context of Bayesian optimization, multi-fidelity modeling, sensitivity analysis, and calibration of computer models. •We introduce GP+ which is an open-source library for kernel-based learning via Gaussian processes (GPs).•We systematically integrate nonlinear manifold learning techniques with GPs’ covariance and mean functions to benefit emulation and low-dimensional visualization.•We enable probabilistic calibration of computer models based on an arbitrary number of data-sources.•We endow GPs with parametric basis functions that accommodate both categorical and numerical features.•We compare GP+ to multiple open-source packages and illustrate its competitive performance.
ISSN:	0965-9978
DOI:	10.1016/j.advengsoft.2024.103686