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dc.contributor.authorGonzález Ibáñez, David
dc.contributor.authorGarcía González, Alberto
dc.contributor.authorChinesta Soria, Francisco
dc.contributor.authorCueto Prendes, Elias
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental
dc.date.accessioned2020-10-13T16:38:53Z
dc.date.available2020-10-13T16:38:53Z
dc.date.issued2020-05-01
dc.identifier.citationGonzález, D. [et al.]. A data-driven learning method for constitutive modeling: application to vascular hyperelastic soft tissues. "Materials", 1 Maig 2020, vol. 13, núm. 10, p. 1-17.
dc.identifier.issn1996-1944
dc.identifier.urihttp://hdl.handle.net/2117/330193
dc.description.abstractWe address the problem of machine learning of constitutive laws when large experimental deviations are present. This is particularly important in soft living tissue modeling, for instance, where large patient-dependent data is found. We focus on two aspects that complicate the problem, namely, the presence of an important dispersion in the experimental results and the need for a rigorous compliance to thermodynamic settings. To address these difficulties, we propose to use, respectively, Topological Data Analysis techniques and a regression over the so-called General Equation for the Nonequilibrium Reversible-Irreversible Coupling (GENERIC) formalism (M. Grmela and H. Ch. Oettinger, Dynamics and thermodynamics of complex fluids. I. Development of a general formalism. Phys. Rev. E 56, 6620, 1997). This allows us, on one hand, to unveil the true “shape” of the data and, on the other, to guarantee the fulfillment of basic principles such as the conservation of energy and the production of entropy as a consequence of viscous dissipation. Examples are provided over pseudo-experimental and experimental data that demonstrate the feasibility of the proposed approach.
dc.format.extent17 p.
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Anàlisi numèrica::Mètodes numèrics
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Anàlisi multivariant
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Programació matemàtica
dc.subject.lcshElasticity
dc.subject.lcshSequences (Mathematics)
dc.subject.lcshProgramming (Mathematics)
dc.subject.othermachine learning
dc.subject.othermanifold learning
dc.subject.othertopological data analysis
dc.subject.otherGENERIC
dc.subject.othersoft living tissues
dc.subject.otherhyperelasticity
dc.subject.othercomputational modeling
dc.titleA data-driven learning method for constitutive modeling: application to vascular hyperelastic soft tissues
dc.typeArticle
dc.subject.lemacElasticitat
dc.subject.lemacSeqüències (Matemàtica)
dc.subject.lemacProgramació (Matemàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. LACÀN - Mètodes Numèrics en Ciències Aplicades i Enginyeria
dc.identifier.doi10.3390/ma13102319
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::74 Mechanics of deformable solids::74A Generalities, axiomatics, foundations of continuum mechanics of solids
dc.subject.amsClassificació AMS::62 Statistics::62L Sequential methods
dc.subject.amsClassificació AMS::90 Operations research, mathematical programming::90C Mathematical programming
dc.relation.publisherversionhttps://www.mdpi.com/1996-1944/13/10/2319
dc.rights.accessOpen Access
local.identifier.drac29004406
dc.description.versionPostprint (published version)
local.citation.authorGonzález, D.; Garcia, A.; Chinesta Soria, Francisco; Cueto, E.
local.citation.publicationNameMaterials
local.citation.volume13
local.citation.number10
local.citation.startingPage1
local.citation.endingPage17


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