Designing accurate emulators for scientific processes using calibration-driven deep models

The success of machine learning for scientific discovery normally depends on how well the inherent assumptions match the problem in hand. Here, Thiagarajan et al. alleviate this constraint by allowing the change of optimization criterion in a data-driven approach to emulate complex scientific proces...

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Autores principales:	Jayaraman J. Thiagarajan, Bindya Venkatesh, Rushil Anirudh, Peer-Timo Bremer, Jim Gaffney, Gemma Anderson, Brian Spears
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/4e1088a77af74dbeb4fcd4fe665cf79c
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spelling	oai:doaj.org-article:4e1088a77af74dbeb4fcd4fe665cf79c2021-12-02T14:41:04ZDesigning accurate emulators for scientific processes using calibration-driven deep models10.1038/s41467-020-19448-82041-1723https://doaj.org/article/4e1088a77af74dbeb4fcd4fe665cf79c2020-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19448-8https://doaj.org/toc/2041-1723The success of machine learning for scientific discovery normally depends on how well the inherent assumptions match the problem in hand. Here, Thiagarajan et al. alleviate this constraint by allowing the change of optimization criterion in a data-driven approach to emulate complex scientific processes.Jayaraman J. ThiagarajanBindya VenkateshRushil AnirudhPeer-Timo BremerJim GaffneyGemma AndersonBrian SpearsNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Jayaraman J. Thiagarajan Bindya Venkatesh Rushil Anirudh Peer-Timo Bremer Jim Gaffney Gemma Anderson Brian Spears Designing accurate emulators for scientific processes using calibration-driven deep models
description	The success of machine learning for scientific discovery normally depends on how well the inherent assumptions match the problem in hand. Here, Thiagarajan et al. alleviate this constraint by allowing the change of optimization criterion in a data-driven approach to emulate complex scientific processes.
format	article
author	Jayaraman J. Thiagarajan Bindya Venkatesh Rushil Anirudh Peer-Timo Bremer Jim Gaffney Gemma Anderson Brian Spears
author_facet	Jayaraman J. Thiagarajan Bindya Venkatesh Rushil Anirudh Peer-Timo Bremer Jim Gaffney Gemma Anderson Brian Spears
author_sort	Jayaraman J. Thiagarajan
title	Designing accurate emulators for scientific processes using calibration-driven deep models
title_short	Designing accurate emulators for scientific processes using calibration-driven deep models
title_full	Designing accurate emulators for scientific processes using calibration-driven deep models
title_fullStr	Designing accurate emulators for scientific processes using calibration-driven deep models
title_full_unstemmed	Designing accurate emulators for scientific processes using calibration-driven deep models
title_sort	designing accurate emulators for scientific processes using calibration-driven deep models
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/4e1088a77af74dbeb4fcd4fe665cf79c
work_keys_str_mv	AT jayaramanjthiagarajan designingaccurateemulatorsforscientificprocessesusingcalibrationdrivendeepmodels AT bindyavenkatesh designingaccurateemulatorsforscientificprocessesusingcalibrationdrivendeepmodels AT rushilanirudh designingaccurateemulatorsforscientificprocessesusingcalibrationdrivendeepmodels AT peertimobremer designingaccurateemulatorsforscientificprocessesusingcalibrationdrivendeepmodels AT jimgaffney designingaccurateemulatorsforscientificprocessesusingcalibrationdrivendeepmodels AT gemmaanderson designingaccurateemulatorsforscientificprocessesusingcalibrationdrivendeepmodels AT brianspears designingaccurateemulatorsforscientificprocessesusingcalibrationdrivendeepmodels
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Designing accurate emulators for scientific processes using calibration-driven deep models

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