000133443 001__ 133443
000133443 005__ 20240416133904.0
000133443 0247_ $$2doi$$a10.1016/j.cma.2024.116961
000133443 0248_ $$2sideral$$a138119
000133443 037__ $$aART-2024-138119
000133443 041__ $$aeng
000133443 100__ $$aLourenço, Rúben
000133443 245__ $$aAn indirect training approach for implicit constitutive modelling using recurrent neural networks and the virtual fields method
000133443 260__ $$c2024
000133443 5060_ $$aAccess copy available to the general public$$fUnrestricted
000133443 5203_ $$aAccurate material description is crucial to achieve high-quality results in computational analysis software. Phenomenological constitutive laws generalize the material behaviour observed in simple mechanical tests. The resulting empirical expressions contain parameters that need to be calibrated through an inverse optimization process. Advancements in Digital Image Correlation (DIC) techniques have enabled the extraction of non-uniform multi-axial displacement fields, facilitating the development of heterogeneous mechanical specimens and unlocking access to richer material behaviour data. Despite these advancements, constitutive models are still limited by mathematical formulations and biases due to simplifying assumptions. Artificial Neural Networks (ANNs), as universal function approximators, could potentially drive a paradigm shift in the field. ANNs do not require explicit pre-formulations, hence avoiding the need for identifying unknown parameters. Moreover, ANNs are able to implicitly learn patterns purely from data, allowing to circumvent the biases induced by the simplifying assumptions of first-order principles. However, the training of ANNs for implicit constitutive modelling is not straightforward, given the impossibility of obtaining direct measurements of stress. This paper explores the integration of Recurrent Neural Networks (RNNs) with the Virtual Fields Method (VFM) for material modelling. This approach uses global force and displacement data to indirectly train the neural network, with the equilibrium being evaluated globally through the VFM loss function. The proposed method is (i) computationally efficient by not requiring Finite Element Analysis (FEA), (ii) compatible with both full-field measurements and numerically generated data, and (iii) able to handle experimental boundary conditions.
000133443 536__ $$9info:eu-repo/grantAgreement/ES/MTFP/TSI-100930-2023-1
000133443 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000133443 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000133443 700__ $$aGeorgieva, Petia
000133443 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, Elías$$uUniversidad de Zaragoza
000133443 700__ $$aAndrade-Campos, A.
000133443 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000133443 773__ $$g425 (2024), 116961 [17 pp.]$$pComput. methods appl. mech. eng.$$tComputer Methods in Applied Mechanics and Engineering$$x0045-7825
000133443 8564_ $$s2085277$$uhttps://zaguan.unizar.es/record/133443/files/texto_completo.pdf$$yVersión publicada
000133443 8564_ $$s2184036$$uhttps://zaguan.unizar.es/record/133443/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000133443 909CO $$ooai:zaguan.unizar.es:133443$$particulos$$pdriver
000133443 951__ $$a2024-04-16-13:15:33
000133443 980__ $$aARTICLE