Hybrid constitutive modeling: data-driven learning of corrections to plasticity models
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 t...
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| Veröffentlicht in: | International journal of material forming 2019-07, Vol.12 (4), p.717-725 |
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| container_issue | 4 |
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| container_title | International journal of material forming |
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| creator | Ibáñez, Rubén Abisset-Chavanne, Emmanuelle González, David Duval, Jean-Louis Cueto, Elias Chinesta, Francisco |
| description | 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. |
| doi_str_mv | 10.1007/s12289-018-1448-x |
| format | Article |
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| ispartof | International journal of material forming, 2019-07, Vol.12 (4), p.717-725 |
| issn | 1960-6206 1960-6214 |
| language | eng |
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| source | SpringerNature Journals |
| subjects | CAE) and Design Computational Intelligence Computer-Aided Engineering (CAD Constitutive models Engineering Engineering Sciences Knowledge acquisition Machine learning Machines Manufacturing Materials Science Mathematical models Mechanical Engineering Modelling Original Research Processes |
| title | Hybrid constitutive modeling: data-driven learning of corrections to plasticity models |
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