Analysis of uncertainty and variability in finite element computational models for biomedical engineering: characterization and propagation
Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis an...
| Autores: | , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2016 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/132648 |
| Acesso em linha: | https://hdl.handle.net/2117/132648 https://dx.doi.org/10.3389/fbioe.2016.00085 |
| Access Level: | acceso abierto |
| Palavra-chave: | Finite element method Uncertainty quantification Finite element models Random variables Intrusive and non-intrusive methods Sampling techniques Computational modeling Elements finits, Mètode dels Àrees temàtiques de la UPC::Enginyeria biomèdica |
| Resumo: | Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering. |
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