Producing chemically accurate atomic Gaussian process regression models by active learning for molecular simulation
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Computational Chemistry"
DOI: 10.1002/jcc.27006
Abstract: Machine learning is becoming increasingly more important in the field of force field development. Never has it been more vital to have chemically accurate machine learning potentials because force fields become more sophisticated and their… read more here.
Keywords: active learning; regression models; chemically accurate; gaussian process ... See more keywords
Adaptive regularized Gaussian process regression for application in the context of hydrogen adsorption on graphene sheets
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Computational Chemistry"
DOI: 10.1002/jcc.27035
Abstract: We present a Gaussian process regression (GPR) scheme with an adaptive regularization scheme applied to the QM7 and QM9 test set, several protonated water clusters and specifically to the problem of atomic hydrogen adsorption on… read more here.
Keywords: adsorption graphene; hydrogen adsorption; graphene sheets; gaussian process ... See more keywords
Evaluation of Matrix Effects in SIMS Using Gaussian Process Regression: The Case of Olivine Mg Isotope Microanalysis
Sign Up to like & getrecommendations! Published in 2025 at "Rapid Communications in Mass Spectrometry"
DOI: 10.1002/rcm.10038
Abstract: Matrix effects by secondary ion mass spectrometry (SIMS) are empirically corrected by calibration using matrix‐matched reference materials. However, conventional parametric regression cannot estimate the prediction uncertainty to account for the difference in compositions of new… read more here.
Keywords: gaussian process; regression; matrix effects; process regression ... See more keywords
Seismic vulnerability of above-ground storage tanks with unanchored support conditions for Na-tech risks based on Gaussian process regression
Sign Up to like & getrecommendations! Published in 2020 at "Bulletin of Earthquake Engineering"
DOI: 10.1007/s10518-020-00960-7
Abstract: This paper aims to investigate the seismic vulnerability of an existing unanchored steel storage tank ideally installed in a refinery in Sicily (Italy), along the lines of performance-based earthquake engineering. Tank performance is estimated by… read more here.
Keywords: storage; process regression; based gaussian; vulnerability ... See more keywords
High-Dimensional Intrinsic Interpolation Using Gaussian Process Regression and Diffusion Maps
Sign Up to like & getrecommendations! Published in 2017 at "Mathematical Geosciences"
DOI: 10.1007/s11004-017-9705-y
Abstract: This article considers the challenging task of estimating geologic properties of interest using a suite of proxy measurements. The current work recast this task as a manifold learning problem. In this process, this article introduces… read more here.
Keywords: high dimensional; process regression; diffusion; process ... See more keywords
Extracting Complex Permittivity of Materials by Gaussian Process Regression Using the Transmission Parameter at Sub-THz
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Electronic Materials"
DOI: 10.1007/s11664-019-07716-3
Abstract: The interaction between electromagnetic waves and materials is related to dielectric properties of materials, which have been used for technological applications in many fields, like polymers. Extracting complex permittivity values can be used to define… read more here.
Keywords: process regression; extracting complex; permittivity; permittivity materials ... See more keywords
An enhanced prediction model for the on-line monitoring of the sensors using the Gaussian process regression
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Mechanical Science and Technology"
DOI: 10.1007/s12206-019-0426-7
Abstract: The auto-associative kernel regression (AAKR) and Gaussian process regression (GPR) have been used for estimating the condition of the sensors in the on-line monitoring system of the nuclear power plants. The estimations of the condition… read more here.
Keywords: regression; gaussian process; process regression; line monitoring ... See more keywords
Improving local pedestrian-level wind environment based on probabilistic assessment using Gaussian process regression
Sign Up to like & getrecommendations! Published in 2021 at "Building and Environment"
DOI: 10.1016/j.buildenv.2021.108172
Abstract: Abstract Many wind comfort assessment standards have been proposed to evaluate wind comfort and safety in spaces with intended use for pedestrians, such as pathways, building entrance areas, amenity spaces, and outdoor sitting spaces. However,… read more here.
Keywords: gaussian process; process regression; wind; environment ... See more keywords
Enhanced variable-fidelity surrogate-based optimization framework by Gaussian process regression and fuzzy clustering
Sign Up to like & getrecommendations! Published in 2020 at "Computer Methods in Applied Mechanics and Engineering"
DOI: 10.1016/j.cma.2020.113045
Abstract: Abstract In order to improve the global optimizing ability of surrogate-based optimizations, an enhanced variable-fidelity surrogate model (VFSM) optimization framework is developed based on the Gaussian process regression algorithm and the fuzzy clustering algorithm, which… read more here.
Keywords: process regression; fidelity; optimization; gaussian process ... See more keywords
Yttrium barium copper oxide superconducting transition temperature modeling through gaussian process regression
Sign Up to like & getrecommendations! Published in 2020 at "Computational Materials Science"
DOI: 10.1016/j.commatsci.2020.109583
Abstract: Abstract The high-temperature superconductor, YBa 2 Cu 3 O 7 - x (YBCO), is a promising candidate for high field magnet fabrication as it has critical temperature, T c , of over 80 K and an… read more here.
Keywords: process regression; temperature; superconducting transition; gaussian process ... See more keywords
Nonparametric Identification of Linear Time-Varying Systems using Gaussian Process Regression
Sign Up to like & getrecommendations! Published in 2020 at "IFAC-PapersOnLine"
DOI: 10.1016/j.ifacol.2020.12.1277
Abstract: Linear time-varying systems are a class of systems, the dynamics of which evolve in time. This results in a time-varying frequency response function where each frequency has a time-varying gain. In classical identification techniques, basis… read more here.
Keywords: time; linear time; process regression; varying systems ... See more keywords