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Invited review: Adaptive numerical modelling and hybrid physically based ANM approaches in materials engineering - a survey

Invited review: Adaptive numerical modelling and hybrid physically based ANM approaches in materials engineering - a survey
Invited review: Adaptive numerical modelling and hybrid physically based ANM approaches in materials engineering - a survey
Many adaptive numerical modelling (ANM) techniques such as artificial neural networks, (including multi-layer perceptrons) support vector machines and Gaussian processes have now been applied to a wide range of regression and classification problems in materials science. Materials science offers a wide range of industrial applications and hence problem complexity levels from well physically characterised systems (e.g. high value, low volume products) to high volume low cost applications with intrinsic scatter due to commercial manufacturing processes. We review a number of recent examples in the literature, with the aim of identifying best practice in the use of these techniques as part of a multi-strand modelling approach. The importance of understanding the basic principles of these modelling techniques and how they can link with other modelling strategies is emphasised. In particular we wish to identify the importance of hybrid physically based-ANM in taking the field forward, which can range from, at the most basic level, careful data selection and data pre-processing to a full integration of physically based models with advanced ANM. A number of case studies are presented to illustrate the main points of the paper.
neural networks, support vector machines, gaussian processes, adaptive numerical modelling, data-driven techniques, hybrid modelling, applications in materials science
0267-0836
488-503
Reed, P.A.S.
8b79d87f-3288-4167-bcfc-c1de4b93ce17
Starink, M.J.
9b959cbf-fd72-4611-9b1a-e0f518907910
Gunn, S.R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Sinclair, I.
157baec6-05bc-47e3-9451-cd15265eb5c8
Reed, P.A.S.
8b79d87f-3288-4167-bcfc-c1de4b93ce17
Starink, M.J.
9b959cbf-fd72-4611-9b1a-e0f518907910
Gunn, S.R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Sinclair, I.
157baec6-05bc-47e3-9451-cd15265eb5c8

Reed, P.A.S., Starink, M.J., Gunn, S.R. and Sinclair, I. (2009) Invited review: Adaptive numerical modelling and hybrid physically based ANM approaches in materials engineering - a survey. Materials Science and Technology, 25 (4), 488-503. (doi:10.1179/174328409X411727).

Record type: Article

Abstract

Many adaptive numerical modelling (ANM) techniques such as artificial neural networks, (including multi-layer perceptrons) support vector machines and Gaussian processes have now been applied to a wide range of regression and classification problems in materials science. Materials science offers a wide range of industrial applications and hence problem complexity levels from well physically characterised systems (e.g. high value, low volume products) to high volume low cost applications with intrinsic scatter due to commercial manufacturing processes. We review a number of recent examples in the literature, with the aim of identifying best practice in the use of these techniques as part of a multi-strand modelling approach. The importance of understanding the basic principles of these modelling techniques and how they can link with other modelling strategies is emphasised. In particular we wish to identify the importance of hybrid physically based-ANM in taking the field forward, which can range from, at the most basic level, careful data selection and data pre-processing to a full integration of physically based models with advanced ANM. A number of case studies are presented to illustrate the main points of the paper.

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Published date: 2009
Additional Information: Imported from ISI Web of Science
Keywords: neural networks, support vector machines, gaussian processes, adaptive numerical modelling, data-driven techniques, hybrid modelling, applications in materials science
Organisations: Engineering Mats & Surface Engineerg Gp

Identifiers

Local EPrints ID: 66200
URI: https://eprints.soton.ac.uk/id/eprint/66200
ISSN: 0267-0836
PURE UUID: 19302049-e25d-4cf2-af58-c435c62700be
ORCID for P.A.S. Reed: ORCID iD orcid.org/0000-0002-2258-0347

Catalogue record

Date deposited: 12 May 2009
Last modified: 20 Jul 2019 01:21

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Contributors

Author: P.A.S. Reed ORCID iD
Author: M.J. Starink
Author: S.R. Gunn
Author: I. Sinclair

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