scholarly journals Correction to: Semi-analytic contact technique in a non-linear parametric model order reduction method for gear simulations

Meccanica ◽  
2021 ◽  
Author(s):  
Mathijs Vivet ◽  
Niccolo Cappellini ◽  
Tommaso Tamarozzi ◽  
Bart Blockmans ◽  
Jakob Fiszer ◽  
...  
Keyword(s):  
Model Order Reduction ◽  
Reduction Method ◽  
Order Reduction ◽  
Parametric Model ◽  
Model Order ◽  
Parametric Model Order Reduction ◽  
Non Linear ◽  
Linear Parametric Model ◽  
Order Reduction Method

scholarly journals Semi-analytic contact technique in a non-linear parametric model order reduction method for gear simulations

Meccanica ◽  
2017 ◽  
Vol 53 (1-2) ◽  
pp. 49-75 ◽  
Author(s):  
Niccolò Cappellini ◽  
Tommaso Tamarozzi ◽  
Bart Blockmans ◽  
Jakob Fiszer ◽  
Francesco Cosco ◽  
...  
Keyword(s):  
Model Order Reduction ◽  
Reduction Method ◽  
Order Reduction ◽  
Parametric Model ◽  
Model Order ◽  
Parametric Model Order Reduction ◽  
Non Linear ◽  
Linear Parametric Model ◽  
Order Reduction Method

scholarly journals On the Interpolation of Reduced-Order Models

2018 ◽  
Author(s):  
Yao Yue ◽  
Lihong Feng ◽  
Peter Benner
Keyword(s):  
Model Order Reduction ◽  
Reduction Method ◽  
Order Reduction ◽  
Reduced Order Models ◽  
Matching Method ◽  
Model Order ◽  
Reduced Order ◽  
Parametric Model Order Reduction ◽  
Order Reduction Method ◽  
Parameter Dependency

A parametric model-order reduction method based on interpolation of reduced-order models, namely the pole-matching method, is proposed for linear systems in the frequency domain. It captures the parametric dynamics of the system by interpolating the positions and amplitudes of the poles. The pole-matching method relies completely on the reduced-order models themselves, regardless of how they are built. It is able to deal with many parameters as well as complicated parameter dependency. Numerical results show that the proposed pole-matching method gives accurate results even when it interpolates two reduced-order models of completely different nature, one computed by a projection-based method and the other computed by a data-driven method.

scholarly journals A Local Basis Approximation Approach for Nonlinear Parametric Model Order Reduction Paper Draft

2021 ◽  
pp. 116055
Author(s):  
Konstantinos Vlachas ◽  
Konstantinos Tatsis ◽  
Konstantinos Agathos ◽  
Adam R. Brink ◽  
Eleni Chatzi
Keyword(s):  
Model Order Reduction ◽  
Order Reduction ◽  
Parametric Model ◽  
Model Order ◽  
Approximation Approach ◽  
Parametric Model Order Reduction ◽  
Local Basis
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