No Cover Image

Journal article 353 views

Comparative study of Transformer- and LSTM-based machine learning methods for transient thermal field reconstruction

Wiera Bielajewa, Michelle Tindall, Perumal Nithiarasu Orcid Logo

Computational Thermal Sciences: An International Journal, Volume: 16, Issue: 3

Swansea University Authors: Wiera Bielajewa, Perumal Nithiarasu Orcid Logo

  • Accepted Manuscript under embargo until: 1st March 2025

Check full text

DOI (Published version): 10.1615/computthermalscien.2023049936

Published in: Computational Thermal Sciences: An International Journal
ISSN: 1940-2503 1940-2554
Published: Begell House 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa65266
first_indexed 2023-12-12T09:19:23Z
last_indexed 2024-11-25T14:15:43Z
id cronfa65266
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2024-04-10T12:29:36.5190018</datestamp><bib-version>v2</bib-version><id>65266</id><entry>2023-12-12</entry><title>Comparative study of Transformer- and LSTM-based machine learning methods for transient thermal field reconstruction</title><swanseaauthors><author><sid>aeac9bf0d7f8e1377e32fdf5143713c5</sid><firstname>Wiera</firstname><surname>Bielajewa</surname><name>Wiera Bielajewa</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>3b28bf59358fc2b9bd9a46897dbfc92d</sid><ORCID>0000-0002-4901-2980</ORCID><firstname>Perumal</firstname><surname>Nithiarasu</surname><name>Perumal Nithiarasu</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2023-12-12</date><abstract/><type>Journal Article</type><journal>Computational Thermal Sciences: An International Journal</journal><volume>16</volume><journalNumber>3</journalNumber><paginationStart/><paginationEnd/><publisher>Begell House</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1940-2503</issnPrint><issnElectronic>1940-2554</issnElectronic><keywords>machine learning, transformer, transient problem, solution reconstruction, conduction, computational heat transfer, sparse measurements</keywords><publishedDay>1</publishedDay><publishedMonth>3</publishedMonth><publishedYear>2024</publishedYear><publishedDate>2024-03-01</publishedDate><doi>10.1615/computthermalscien.2023049936</doi><url/><notes/><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><apcterm/><funders>This work was part-funded by the United Kingdom Atomic Energy Authority (UKAEA) and the Engineering and Physical Sciences Research Council (EPSRC) under Grant Agreement Numbers EP/W006839/1, EP/T517987/1 and EP/R012091/1. We acknowledge the support of Supercomputing Wales and AccelerateAI projects, which is partfunded by the European Regional Development Fund (ERDF) via the Welsh Government for giving us access to NVIDIA A100 40GB GPUs for batch training.</funders><projectreference/><lastEdited>2024-04-10T12:29:36.5190018</lastEdited><Created>2023-12-12T09:14:50.2263418</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Biomedical Engineering</level></path><authors><author><firstname>Wiera</firstname><surname>Bielajewa</surname><order>1</order></author><author><firstname>Michelle</firstname><surname>Tindall</surname><order>2</order></author><author><firstname>Perumal</firstname><surname>Nithiarasu</surname><orcid>0000-0002-4901-2980</orcid><order>3</order></author></authors><documents><document><filename>Under embargo</filename><originalFilename>Under embargo</originalFilename><uploaded>2023-12-12T09:18:47.5855496</uploaded><type>Output</type><contentLength>34229833</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2025-03-01T00:00:00.0000000</embargoDate><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2024-04-10T12:29:36.5190018 v2 65266 2023-12-12 Comparative study of Transformer- and LSTM-based machine learning methods for transient thermal field reconstruction aeac9bf0d7f8e1377e32fdf5143713c5 Wiera Bielajewa Wiera Bielajewa true false 3b28bf59358fc2b9bd9a46897dbfc92d 0000-0002-4901-2980 Perumal Nithiarasu Perumal Nithiarasu true false 2023-12-12 Journal Article Computational Thermal Sciences: An International Journal 16 3 Begell House 1940-2503 1940-2554 machine learning, transformer, transient problem, solution reconstruction, conduction, computational heat transfer, sparse measurements 1 3 2024 2024-03-01 10.1615/computthermalscien.2023049936 COLLEGE NANME COLLEGE CODE Swansea University This work was part-funded by the United Kingdom Atomic Energy Authority (UKAEA) and the Engineering and Physical Sciences Research Council (EPSRC) under Grant Agreement Numbers EP/W006839/1, EP/T517987/1 and EP/R012091/1. We acknowledge the support of Supercomputing Wales and AccelerateAI projects, which is partfunded by the European Regional Development Fund (ERDF) via the Welsh Government for giving us access to NVIDIA A100 40GB GPUs for batch training. 2024-04-10T12:29:36.5190018 2023-12-12T09:14:50.2263418 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Wiera Bielajewa 1 Michelle Tindall 2 Perumal Nithiarasu 0000-0002-4901-2980 3 Under embargo Under embargo 2023-12-12T09:18:47.5855496 Output 34229833 application/pdf Accepted Manuscript true 2025-03-01T00:00:00.0000000 true eng
title Comparative study of Transformer- and LSTM-based machine learning methods for transient thermal field reconstruction
spellingShingle Comparative study of Transformer- and LSTM-based machine learning methods for transient thermal field reconstruction
Wiera Bielajewa
Perumal Nithiarasu
title_short Comparative study of Transformer- and LSTM-based machine learning methods for transient thermal field reconstruction
title_full Comparative study of Transformer- and LSTM-based machine learning methods for transient thermal field reconstruction
title_fullStr Comparative study of Transformer- and LSTM-based machine learning methods for transient thermal field reconstruction
title_full_unstemmed Comparative study of Transformer- and LSTM-based machine learning methods for transient thermal field reconstruction
title_sort Comparative study of Transformer- and LSTM-based machine learning methods for transient thermal field reconstruction
author_id_str_mv aeac9bf0d7f8e1377e32fdf5143713c5
3b28bf59358fc2b9bd9a46897dbfc92d
author_id_fullname_str_mv aeac9bf0d7f8e1377e32fdf5143713c5_***_Wiera Bielajewa
3b28bf59358fc2b9bd9a46897dbfc92d_***_Perumal Nithiarasu
author Wiera Bielajewa
Perumal Nithiarasu
author2 Wiera Bielajewa
Michelle Tindall
Perumal Nithiarasu
format Journal article
container_title Computational Thermal Sciences: An International Journal
container_volume 16
container_issue 3
publishDate 2024
institution Swansea University
issn 1940-2503
1940-2554
doi_str_mv 10.1615/computthermalscien.2023049936
publisher Begell House
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
document_store_str 0
active_str 0
published_date 2024-03-01T08:26:59Z
_version_ 1821393309311959040
score 11.04776

Similar Items