A Manifold Learning Approach to Data-Driven Computational Elasticity and Inelasticity
Resumen: Standard simulation in classical mechanics is based on the use of two very different types of equations. The first one, of axiomatic character, is related to balance laws (momentum, mass, energy, ...), whereas the second one consists of models that scientists have extracted from collected, natural or synthetic data. Even if one can be confident on the first type of equations, the second one contains modeling errors. Moreover, this second type of equations remains too particular and often fails in describing new experimental results. The vast majority of existing models lack of generality, and therefore must be constantly adapted or enriched to describe new experimental findings. In this work we propose a new method, able to directly link data to computers in order to perform numerical simulations. These simulations will employ axiomatic, universal laws while minimizing the need of explicit, often phenomenological, models. This technique is based on the use of manifold learning methodologies, that allow to extract the relevant information from large experimental datasets.
Idioma: Inglés
DOI: 10.1007/s11831-016-9197-9
Año: 2016
ISSN: 1134-3060

Financiación: info:eu-repo/grantAgreement/ES/DGA/T88
Financiación: info:eu-repo/grantAgreement/ES/MINECO/DPI2014-51844-C2-1-2-R
Financiación: info:eu-repo/grantAgreement/ES/MINECO/DPI2015-72365-EXP
Tipo y forma: Artículo (PostPrint)
Área (Departamento): Mec. de Medios Contínuos y Teor. de Estructuras (Departamento de Ingeniería Mecánica)

Derechos Reservados Derechos reservados por el editor de la revista

Exportado de SIDERAL (2017-06-01-13:58:31)

Este artículo se encuentra en las siguientes colecciones:
Artículos > Artículos por área > Mec. de Medios Contínuos y Teor. de Estructuras

 Registro creado el 2017-03-16, última modificación el 2017-06-01

Valore este documento:

Rate this document:
(Sin ninguna reseña)