000084214 001__ 84214
000084214 005__ 20200513005823.0
000084214 0247_ $$2doi$$a10.1007/s12289-018-1448-x
000084214 0248_ $$2sideral$$a108576
000084214 037__ $$aART-2018-108576
000084214 041__ $$aeng
000084214 100__ $$aIbáñez, R.
000084214 245__ $$aHybrid constitutive modeling: data-driven learning of corrections to plasticity models
000084214 260__ $$c2018
000084214 5060_ $$aAccess copy available to the general public$$fUnrestricted
000084214 5203_ $$aIn 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.
000084214 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T24-17R$$9info: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/AdMoRe$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 675919-AdMoRe$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2015-72365-EXP$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2017-85139-C2-1-R
000084214 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000084214 590__ $$a1.75$$b2018
000084214 591__ $$aMETALLURGY & METALLURGICAL ENGINEERING$$b27 / 76 = 0.355$$c2018$$dQ2$$eT2
000084214 591__ $$aENGINEERING, MANUFACTURING$$b36 / 49 = 0.735$$c2018$$dQ3$$eT3
000084214 591__ $$aMATERIALS SCIENCE, MULTIDISCIPLINARY$$b190 / 293 = 0.648$$c2018$$dQ3$$eT2
000084214 592__ $$a0.638$$b2018
000084214 593__ $$aMaterials Science (miscellaneous)$$c2018$$dQ2
000084214 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000084214 700__ $$aAbisset-Chavanne, E.
000084214 700__ $$0(orcid)0000-0003-3003-5856$$aGonzález, D.$$uUniversidad de Zaragoza
000084214 700__ $$aDuval, J.L.
000084214 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, E.$$uUniversidad de Zaragoza
000084214 700__ $$aChinesta, F.
000084214 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000084214 773__ $$g12 (2018), 717 – 725$$pInt.J.Mater.Form.$$tInternational journal of material forming$$x1960-6206
000084214 8564_ $$s2218282$$uhttps://zaguan.unizar.es/record/84214/files/texto_completo.pdf$$yPostprint
000084214 8564_ $$s11606$$uhttps://zaguan.unizar.es/record/84214/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000084214 909CO $$ooai:zaguan.unizar.es:84214$$particulos$$pdriver
000084214 951__ $$a2020-05-13-00:50:42
000084214 980__ $$aARTICLE