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Nonlinear manifold learning for model reduction in finite elastodynamics

Autor
Millán, D.; Arroyo, M.
Tipus d'activitat
Article en revista
Revista
Computer methods in applied mechanics and engineering
Data de publicació
2013
Volum
261-262
Pàgina inicial
118
Pàgina final
131
DOI
https://doi.org/10.1016/j.cma.2013.04.007 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/78632 Obrir en finestra nova
Resum
Model reduction in computational mechanics is generally addressed with linear dimensionality reduction methods such as Principal Components Analysis (PCA). Hypothesizing that in many applications of interest the essential dynamics evolve on a nonlinear manifold, we explore here reduced order modeling based on nonlinear dimen- sionality reduction methods. Such methods are gaining popularity in diverse fields of science and technology, such as machine perception or molecular simulation. We conside...
Citació
Millán, D., Arroyo, M. Nonlinear manifold learning for model reduction in finite elastodynamics. "Computer methods in applied mechanics and engineering", 2013, vol. 261-262, p. 118-131.
Paraules clau
Reduced Order Modeling, Nonlinear Dimensionality Reduction, Finite Deformation Elastodynamics, Maximum Entropy Approximants, Variational Integrators
Grup de recerca
LACÀN - Mètodes Numèrics en Ciències Aplicades i Enginyeria

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