doi:10.1007/s12289-018-1448-xengIbáñez, R.Abisset-Chavanne, E.González, D.Duval, J.L.Cueto, E.Chinesta, F.Hybrid constitutive modeling: data-driven learning of corrections to plasticity modelsART-2018-108576In 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.2018http://zaguan.unizar.es/record/8421410.1007/s12289-018-1448-xhttp://zaguan.unizar.es/record/84214oai:zaguan.unizar.es:84214info:eu-repo/grantAgreement/ES/DGA/T24-17Rinfo:eu-repo/grantAgreement/EC/H2020/675919/EU/Empowered decision-making in simulation-based engineering: Advanced Model Reduction for real-time, inverse and optimization in industrial problems/AdMoReThis project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 675919-AdMoReinfo:eu-repo/grantAgreement/ES/MINECO/DPI2015-72365-EXPinfo:eu-repo/grantAgreement/ES/MINECO/DPI2017-85139-C2-1-RInternational journal of material forming 12 (2018), 717 – 725All rights reservedhttp://www.europeana.eu/rights/rr-f/info:eu-repo/semantics/openAccess