On Shape Deformation Techniques for Simulation-Based Design Optimization

2015 ◽  
pp. 281-303 ◽  
Author(s):  
Daniel Sieger ◽  
Stefan Menzel ◽  
Mario Botsch
Keyword(s):  
Design Optimization ◽  
Shape Deformation ◽  
Simulation Based Design ◽  
Simulation Based

scholarly journals Sound simulation-based design optimization of brass wind instruments

2019 ◽  
Vol 145 (6) ◽  
pp. 3795-3804 ◽  
Author(s):  
Robin Tournemenne ◽  
Jean-François Petiot ◽  
Bastien Talgorn ◽  
Joël Gilbert ◽  
Michael Kokkolaras
Keyword(s):  
Design Optimization ◽  
Wind Instruments ◽  
Simulation Based Design ◽  
Simulation Based ◽  
Sound Simulation

RBF morphing techniques for simulation-based design optimization

2013 ◽  
Vol 30 (2) ◽  
pp. 161-174 ◽  
Author(s):  
Daniel Sieger ◽  
Stefan Menzel ◽  
Mario Botsch
Keyword(s):  
Design Optimization ◽  
Simulation Based Design ◽  
Simulation Based

Problem Formulations for Simulation-Based Design Optimization Using Statistical Surrogates and Direct Search

2014 ◽  
Author(s):  
Bastien Talgorn ◽  
Sébastien Le Digabel ◽  
Michael Kokkolaras
Keyword(s):  
Design Optimization ◽  
Computational Models ◽  
Optimization Problems ◽  
Direct Search ◽  
Local Optima ◽  
Numerical Noise ◽  
Derivative Free Optimization ◽  
Simulation Based Design ◽  
Derivative Free ◽  
Simulation Based

Typical challenges of simulation-based design optimization include unavailable gradients and unreliable approximations thereof, expensive function evaluations, numerical noise, multiple local optima and the failure of the analysis to return a value to the optimizer. The remedy for all these issues is to use surrogate models in lieu of the computational models or simulations and derivative-free optimization algorithms. In this work, we use the R dynaTree package to build statistical surrogates of the blackboxes and the direct search method for derivative-free optimization. We present different formulations for the surrogate problem considered at each search step of the Mesh Adaptive Direct Search (MADS) algorithm using a surrogate management framework. The proposed formulations are tested on two simulation-based multidisciplinary design optimization problems. Numerical results confirm that the use of statistical surrogates in MADS improves the efficiency of the optimization algorithm.

Statistical Surrogate Formulations for Simulation-Based Design Optimization

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Bastien Talgorn ◽  
Sébastien Le Digabel ◽  
Michael Kokkolaras
Keyword(s):  
Design Optimization ◽  
Computational Models ◽  
Direct Search ◽  
Benchmark Problems ◽  
Local Optima ◽  
Numerical Noise ◽  
Derivative Free Optimization ◽  
Simulation Based Design ◽  
Derivative Free ◽  
Simulation Based

Typical challenges of simulation-based design optimization include unavailable gradients and unreliable approximations thereof, expensive function evaluations, numerical noise, multiple local optima, and the failure of the analysis to return a value to the optimizer. One possible remedy to alleviate these issues is to use surrogate models in lieu of the computational models or simulations and derivative-free optimization algorithms. In this work, we use the R dynaTree package to build statistical surrogates of the blackboxes and the direct search method for derivative-free optimization. We present different formulations for the surrogate problem (SP) considered at each search step of the mesh adaptive direct search (MADS) algorithm using a surrogate management framework. The proposed formulations are tested on 20 analytical benchmark problems and two simulation-based multidisciplinary design optimization (MDO) problems. Numerical results confirm that the use of statistical surrogates in MADS improves the efficiency of the optimization algorithm.

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