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A data-driven learning method for constitutive modeling: application to vascular hyperelastic soft tissues
dc.contributor.author | González Ibáñez, David |
dc.contributor.author | García González, Alberto |
dc.contributor.author | Chinesta Soria, Francisco |
dc.contributor.author | Cueto Prendes, Elias |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental |
dc.date.accessioned | 2020-10-13T16:38:53Z |
dc.date.available | 2020-10-13T16:38:53Z |
dc.date.issued | 2020-05-01 |
dc.identifier.citation | González, 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.issn | 1996-1944 |
dc.identifier.uri | http://hdl.handle.net/2117/330193 |
dc.description.abstract | We 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.extent | 17 p. |
dc.language.iso | eng |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Anàlisi numèrica::Mètodes numèrics |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Anàlisi multivariant |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Programació matemàtica |
dc.subject.lcsh | Elasticity |
dc.subject.lcsh | Sequences (Mathematics) |
dc.subject.lcsh | Programming (Mathematics) |
dc.subject.other | machine learning |
dc.subject.other | manifold learning |
dc.subject.other | topological data analysis |
dc.subject.other | GENERIC |
dc.subject.other | soft living tissues |
dc.subject.other | hyperelasticity |
dc.subject.other | computational modeling |
dc.title | A data-driven learning method for constitutive modeling: application to vascular hyperelastic soft tissues |
dc.type | Article |
dc.subject.lemac | Elasticitat |
dc.subject.lemac | Seqüències (Matemàtica) |
dc.subject.lemac | Programació (Matemàtica) |
dc.contributor.group | Universitat Politècnica de Catalunya. LACÀN - Mètodes Numèrics en Ciències Aplicades i Enginyeria |
dc.identifier.doi | 10.3390/ma13102319 |
dc.description.peerreviewed | Peer Reviewed |
dc.subject.ams | Classificació AMS::74 Mechanics of deformable solids::74A Generalities, axiomatics, foundations of continuum mechanics of solids |
dc.subject.ams | Classificació AMS::62 Statistics::62L Sequential methods |
dc.subject.ams | Classificació AMS::90 Operations research, mathematical programming::90C Mathematical programming |
dc.relation.publisherversion | https://www.mdpi.com/1996-1944/13/10/2319 |
dc.rights.access | Open Access |
local.identifier.drac | 29004406 |
dc.description.version | Postprint (published version) |
local.citation.author | González, D.; Garcia, A.; Chinesta Soria, Francisco; Cueto, E. |
local.citation.publicationName | Materials |
local.citation.volume | 13 |
local.citation.number | 10 |
local.citation.startingPage | 1 |
local.citation.endingPage | 17 |
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