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...

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Vydáno v:Advances in engineering software (1992) Ročník 195; s. 103686
Hlavní autoři: Yousefpour, Amin, Foumani, Zahra Zanjani, Shishehbor, Mehdi, Mora, Carlos, Bostanabad, Ramin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.09.2024
Témata:
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
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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