期刊
INTERNATIONAL JOURNAL OF MATERIAL FORMING
卷 12, 期 4, 页码 717-725出版社
SPRINGER FRANCE
DOI: 10.1007/s12289-018-1448-x
关键词
Machine learning; Data-driven computational mechanics; Plasticity; Model learning
类别
资金
- European Union [675919]
- Spanish Ministry of Economy and Competitiveness [DPI2017-85139-C2-1-R, DPI2015-72365-EXP]
- Regional Government of Aragon
- European Social Fund [T24 17R]
In recent times a growing interest has arose on the development of data-driven techniques to avoid the employ of phenomenological constitutive models. While it is true that, in general, data do not fit perfectly to existing models, and present deviations from the most popular ones, we believe that this does not justify (or, at least, not always) to abandon completely all the acquired knowledge on the constitutive characterization of materials. Instead, what we propose here is, by means of machine learning techniques, to develop correction to those popular models so as to minimize the errors in constitutive modeling.
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