Journal article 636 views 319 downloads
Deep learning or interpolation for inverse modelling of heat and fluid flow problems?
International Journal of Numerical Methods for Heat & Fluid Flow, Volume: 31, Issue: 9, Pages: 3036 - 3046
Swansea University Authors:
Hamid Tamaddon-Jahromi, Neeraj Kavan Chakshu, Perumal Nithiarasu
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DOI (Published version): 10.1108/hff-11-2020-0684
Abstract
PurposeThe purpose of this study is to compare interpolation algorithms and deep neural networks for inverse transfer problems with linear and nonlinear behaviour.Design/methodology/approachA series of runs were conducted for a canonical test problem. These were used as databases or “learning sets”...
| Published in: | International Journal of Numerical Methods for Heat & Fluid Flow |
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| ISSN: | 0961-5539 |
| Published: |
Emerald
2021
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa55843 |
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| Abstract: |
PurposeThe purpose of this study is to compare interpolation algorithms and deep neural networks for inverse transfer problems with linear and nonlinear behaviour.Design/methodology/approachA series of runs were conducted for a canonical test problem. These were used as databases or “learning sets” for both interpolation algorithms and deep neural networks. A second set of runs was conducted to test the prediction accuracy of both approaches.FindingsThe results indicate that interpolation algorithms outperform deep neural networks in accuracy for linear heat conduction, while the reverse is true for nonlinear heat conduction problems. For heat convection problems, both methods offer similar levels of accuracy.Originality/valueThis is the first time such a comparison has been made. |
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| Keywords: |
Interpolation, Deep Learning, Deep Neural Networks, Linear heat conduction, Non-Linear heat conduction, Forced and natural convection |
| College: |
Faculty of Science and Engineering |
| Issue: |
9 |
| Start Page: |
3036 |
| End Page: |
3046 |