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Deep learning or interpolation for inverse modelling of heat and fluid flow problems? / Rainald Löhner; Harbir Antil; Hamid Tamaddon-Jahromi; Neeraj Kavan Chakshu; Perumal Nithiarasu

International Journal of Numerical Methods for Heat & Fluid Flow, Volume: ahead-of-print, Issue: ahead-of-print

Swansea University Authors: Hamid, Tamaddon-Jahromi, Neeraj Kavan, Chakshu, Perumal, Nithiarasu

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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”...

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Published in: International Journal of Numerical Methods for Heat & Fluid Flow
ISSN: 0961-5539
Published: Emerald 2021
Online Access: Check full text

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.
Keywords: Interpolation, Deep Learning, Deep Neural Networks, Linear heat conduction, Non-Linear heat conduction, Forced and natural convection
College: College of Engineering
Issue: ahead-of-print