Journal
INTERNATIONAL JOURNAL OF NUMERICAL METHODS FOR HEAT & FLUID FLOW
Volume 31, Issue 9, Pages 3036-3046Publisher
EMERALD GROUP PUBLISHING LTD
DOI: 10.1108/HFF-11-2020-0684
Keywords
Machine learning; Deep neural networks; Forced and natural convection; Interpolation; Linear heat conduction; Nonlinear heat conduction; Deep learning
Funding
- NSF [DMS-1818772, DMS-1913004]
- Air Force Office of Scientific Research (AFOSR) [FA9550-19-1-0036]
- Department of Navy, Naval Postgraduate School [N00244-20-1-0005]
- Ser Cymru III - Tackling Covid-19 fund, Welsh Government [095]
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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.
Purpose The purpose of this study is to compare interpolation algorithms and deep neural networks for inverse transfer problems with linear and nonlinear behaviour. Design/methodology/approach A 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. Findings The 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/value This is the first time such a comparison has been made.
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