000146931 001__ 146931 000146931 005__ 20241129141114.0 000146931 0247_ $$2doi$$a10.1108/EC-12-2023-0958 000146931 0248_ $$2sideral$$a140787 000146931 037__ $$aART-2024-140787 000146931 041__ $$aeng 000146931 100__ $$aDi Lorenzo, Daniele 000146931 245__ $$aPhysics-informed and graph neural networks for enhanced inverse analysis 000146931 260__ $$c2024 000146931 5060_ $$aAccess copy available to the general public$$fUnrestricted 000146931 5203_ $$aPurpose : This paper presents an original approach for learning models, partially known, of particular interest when performing source identification or structural health monitoring. The proposed procedures employ some amount of knowledge on the system under scrutiny as well as a limited amount of data efficiently assimilated. Design/methodology/approach : Two different formulations are explored. The first, based on the use of informed neural networks, leverages data collected at specific locations and times to determine the unknown source term of a parabolic partial differential equation. The second procedure, more challenging, involves learning the unknown model from a single measured field history, enabling the localization of a region where material properties differ. Findings: Both procedures assume some kind of sparsity, either in the source distribution or in the region where physical properties differ. This paper proposed two different neural approaches able to learn models in order to perform efficient inverse analyses.Originality/valueTwo original methodologies are explored to identify hidden property that can be recovered with the right usage of data. Both methodologies are based on neural network architecture. Originality/value : Two original methodologies are explored to identify hidden property that can be recovered with the right usage of data. Both methodologies are based on neural network architecture. 000146931 536__ $$9info:eu-repo/grantAgreement/EC/H2020/956401/EU/Cross-scale concurrent material-structure design using functionally-graded 3D-printed matematerials/XS-Meta$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 956401-XS-Meta 000146931 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000146931 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000146931 700__ $$aChampaney, Victor 000146931 700__ $$aGhnatios, Chady 000146931 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, Elías$$uUniversidad de Zaragoza 000146931 700__ $$aChinesta, Francisco 000146931 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est. 000146931 773__ $$g(2024), [29 pp.]$$pEng. comput.$$tENGINEERING COMPUTATIONS$$x0264-4401 000146931 8564_ $$s14127637$$uhttps://zaguan.unizar.es/record/146931/files/texto_completo.pdf$$yVersión publicada 000146931 8564_ $$s1917107$$uhttps://zaguan.unizar.es/record/146931/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000146931 909CO $$ooai:zaguan.unizar.es:146931$$particulos$$pdriver 000146931 951__ $$a2024-11-29-13:24:33 000146931 980__ $$aARTICLE