Computational Technology Resources - CCC

Y. El Assami, B. Gely, "Using neural networks for dimensioning and certification of mechanical systems: model precision and accuracy", in B.H.V. Topping, J. Kruis, (Editors), "Proceedings of the Fourteenth International Conference on Computational Structures Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 3, Paper 4.3, 2022, doi:10.4203/ccc.3.4.3

Keywords: model approximation, machine learning, neural networks, deep learning, model precision, mechanical structures.

Abstract

Neural networks show impressive performance in lots of domains to handle problems of high complexity. They are universal approximators and can, in principle, be used to learn any type of model. Their use would be of a great benefit as they can be intended to automate major tasks within an engineering project (such as system dimensioning, certification, and criteria verification). However, it is not yet customary to use these technics for lack of competitiveness against forward engineering calculations. One major issue is the robustness and the difficulty to ensure high precisions for deterministic predictions. In this work, we investigate the ability of neural networks to be used to approximate engineering models and their performance in terms of precision and accuracy per target relative error. Increasing accuracy requires understanding how these models work in a deeper way. Applications on use-cases of mechanical structures are used to understand the behaviour of neural networks for this type of problems and illustrate the encountered constraints

download the full-text of this paper (PDF, 6 pages, 328 Kb)

go to the previous paper
go to the next paper
return to the table of contents
return to the volume description

	Computational & Technology Resources an online resource for computational, engineering & technology publications
	not logged in - login
Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 3 PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY Edited by: B.H.V. Topping and J. Kruis Paper 4.3 Using neural networks for dimensioning and certification of mechanical systems: model precision and accuracy Y. El Assami and B. Gely Technology and Engineering Center, Capgemini Engineering Blagnac, France doi:10.4203/ccc.3.4.3 Full Bibliographic Reference for this paper Y. El Assami, B. Gely, "Using neural networks for dimensioning and certification of mechanical systems: model precision and accuracy", in B.H.V. Topping, J. Kruis, (Editors), "Proceedings of the Fourteenth International Conference on Computational Structures Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 3, Paper 4.3, 2022, doi:10.4203/ccc.3.4.3 Keywords: model approximation, machine learning, neural networks, deep learning, model precision, mechanical structures. Abstract Neural networks show impressive performance in lots of domains to handle problems of high complexity. They are universal approximators and can, in principle, be used to learn any type of model. Their use would be of a great benefit as they can be intended to automate major tasks within an engineering project (such as system dimensioning, certification, and criteria verification). However, it is not yet customary to use these technics for lack of competitiveness against forward engineering calculations. One major issue is the robustness and the difficulty to ensure high precisions for deterministic predictions. In this work, we investigate the ability of neural networks to be used to approximate engineering models and their performance in terms of precision and accuracy per target relative error. Increasing accuracy requires understanding how these models work in a deeper way. Applications on use-cases of mechanical structures are used to understand the behaviour of neural networks for this type of problems and illustrate the encountered constraints download the full-text of this paper (PDF, 6 pages, 328 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
Back to top	©Civil-Comp Limited 2023 - terms & conditions