Reed, P.A.S., Starink, M.J., Gunn, S.R. and Sinclair, I.
Invited review: Adaptive numerical modelling and hybrid physically based ANM approaches in materials engineering - a survey
Materials Science and Technology, 25, (4), . (doi:10.1179/174328409X411727).
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.
|Digital Object Identifier (DOI):
||neural networks, support vector machines, gaussian processes, adaptive numerical modelling, data-driven techniques, hybrid modelling, applications in materials science
||Engineering Mats & Surface Engineerg Gp
||12 May 2009
||18 Apr 2017 21:43
|Further Information:||Google Scholar|
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