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 
Computational Thermal Sciences: An International Journal, Volume: 16, Issue: 3
Swansea University Authors:
Wiera Bielajewa, Perumal Nithiarasu
DOI (Published version): 10.1615/computthermalscien.2023049936
Abstract
Comparative study of Transformer- and LSTM-based machine learning methods for transient thermal field reconstruction
| Published in: | Computational Thermal Sciences: An International Journal |
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| ISSN: | 1940-2503 1940-2554 |
| Published: |
Begell House
2024
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa65266 |
| Keywords: |
machine learning, transformer, transient problem, solution reconstruction, conduction, computational heat transfer, sparse measurements |
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| College: |
Faculty of Science and Engineering |
| 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. |
| Issue: |
3 |