Model order reduction for hyperelastic materials
By Siamak Niroomandi, Icíar Alfaro, Elías Cueto, Francisco Chinesta. 2010
77 citations recorded by the source corpus.
In this paper, we develop a novel algorithm for the dimensional reduction of the models of hyperelastic solids undergoing large strains. Unlike standard proper orthogonal decomposition methods, the proposed algorithm minimizes the use of the Newton algorithms in the search of non‐linear equilibrium paths of elastic bodies.
Subfields: Biomedical Engineering, Statistical and Nonlinear Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/b194e098b17fd12dd66179ec50eaf3b97dcabede
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
In Statistical and Nonlinear Physics
- Amine Ammar
- Elías Cueto
- Francisco Chinesta
- Anthony Nouy
- Karen E. Willcox
- Benjamin Peherstorfer
- Max D. Gunzburger
In Statistics and Probability
- Amine Ammar
- Elías Cueto
- Francisco Chinesta
- Anthony Nouy
- Antonio Falcó
- Antonio Huerta
- Sergey V. Dolgov
- Benjamin Peherstorfer
Citations in the corpus
- Recent Advances and New Challenges in the Use of the Proper Generalized Decomposition for Solving Multidimensional Models (2010, methodology): “In [36, 37] and [38] we explored the use of model reduction techniques based on the use of Proper Orthogonal Decompositions (POD) combined with an advanced non-linear solver (the asymptotic numerical method) that avoids the necessity of recomputing the tangent matrix at each load updating.”
- Simulation of hyperelastic materials in real-time using Deep Learning (2019, unknown): “To precisely account for non-linearities it might be necessary to recompute the entire stiffness matrix which is burdensome and not always possible[8].”