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Title: Multi-fidelity classification using Gaussian processes: Accelerating the prediction of large-scale computational models

Authors:
ORCiD logo; ORCiD logo; ORCiD logo; ORCiD logo
Publication Date:
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1564273
Grant/Contract Number:  
SC0019116
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Computer Methods in Applied Mechanics and Engineering
Additional Journal Information:
Journal Name: Computer Methods in Applied Mechanics and Engineering Journal Volume: 357 Journal Issue: C; Journal ID: ISSN 0045-7825
Publisher:
Elsevier
Country of Publication:
Netherlands
Language:
English

Citation Formats

Sahli Costabal, Francisco, Perdikaris, Paris, Kuhl, Ellen, and Hurtado, Daniel E. Multi-fidelity classification using Gaussian processes: Accelerating the prediction of large-scale computational models. Netherlands: N. p., 2019. Web. doi:10.1016/j.cma.2019.112602.
Sahli Costabal, Francisco, Perdikaris, Paris, Kuhl, Ellen, & Hurtado, Daniel E. Multi-fidelity classification using Gaussian processes: Accelerating the prediction of large-scale computational models. Netherlands. doi:10.1016/j.cma.2019.112602.
Sahli Costabal, Francisco, Perdikaris, Paris, Kuhl, Ellen, and Hurtado, Daniel E. Sun . "Multi-fidelity classification using Gaussian processes: Accelerating the prediction of large-scale computational models". Netherlands. doi:10.1016/j.cma.2019.112602.
@article{osti_1564273,
title = {Multi-fidelity classification using Gaussian processes: Accelerating the prediction of large-scale computational models},
author = {Sahli Costabal, Francisco and Perdikaris, Paris and Kuhl, Ellen and Hurtado, Daniel E.},
abstractNote = {},
doi = {10.1016/j.cma.2019.112602},
journal = {Computer Methods in Applied Mechanics and Engineering},
number = C,
volume = 357,
place = {Netherlands},
year = {2019},
month = {12}
}

Journal Article:
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This content will become publicly available on August 30, 2020
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