A Comprehensive Approach to Cataloging Missile Aerodynamic Performance using Surrogate Modeling Techniques and Statistical Learning

2011 ◽  
Author(s):  
Mark Carpenter ◽  
Roy Hartfield ◽  
John Burkhalter
Keyword(s):  
Statistical Learning ◽  
Surrogate Modeling ◽  
Aerodynamic Performance ◽  
Comprehensive Approach ◽  
Modeling Techniques

A methodology for auto-calibrating urban building energy models using surrogate modeling techniques

2018 ◽  
Vol 12 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Shreshth Nagpal ◽  
Caitlin Mueller ◽  
Arfa Aijazi ◽  
Christoph F. Reinhart
Keyword(s):  
Surrogate Modeling ◽  
Building Energy ◽  
Energy Models ◽  
Modeling Techniques

Mixed Variable Gaussian Process-Based Surrogate Modeling Techniques: Application to Aerospace Design

2021 ◽  
pp. 1-25
Author(s):  
Julien Pelamatti ◽  
Loïc Brevault ◽  
Mathieu Balesdent ◽  
El-Ghazali Talbi ◽  
Yannick Guerin
Keyword(s):  
Gaussian Process ◽  
Surrogate Modeling ◽  
Modeling Techniques ◽  
Mixed Variable

scholarly journals Selecting the Best Quantity and Variety of Surrogates for an Ensemble Model

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1721
Author(s):  
Pengcheng Ye ◽  
Guang Pan
Keyword(s):  
Surrogate Modeling ◽  
Surrogate Models ◽  
Prediction Performance ◽  
Basis Functions ◽  
Ensemble Model ◽  
Ensemble Models ◽  
Modeling Techniques ◽  
Mathematical Problems ◽  
Ensemble Of Surrogates ◽  
Polynomial Response Surface

Surrogate modeling techniques are widely used to replace the computationally expensive black-box functions in engineering. As a combination of individual surrogate models, an ensemble of surrogates is preferred due to its strong robustness. However, how to select the best quantity and variety of surrogates for an ensemble has always been a challenging task. In this work, five popular surrogate modeling techniques including polynomial response surface (PRS), radial basis functions (RBF), kriging (KRG), Gaussian process (GP) and linear shepard (SHEP) are considered as the basic surrogate models, resulting in twenty-six ensemble models by using a previously presented weights selection method. The best ensemble model is expected to be found by comparative studies on prediction accuracy and robustness. By testing eight mathematical problems and two engineering examples, we found that: (1) in general, using as many accurate surrogates as possible to construct ensemble models will improve the prediction performance and (2) ensemble models can be used as an insurance rather than offering significant improvements. Moreover, the ensemble of three surrogates PRS, RBF and KRG is preferred based on the prediction performance. The results provide engineering practitioners with guidance on the superior choice of the quantity and variety of surrogates for an ensemble.

Appraisal of Surrogate Modeling Techniques: A Case Study of Electromagnetic Device

2013 ◽  
Vol 49 (5) ◽  
pp. 1993-1996 ◽  
Author(s):  
Marcus H. S. Mendes ◽  
Gustavo L. Soares ◽  
Jean-Louis Coulomb ◽  
Joao A. Vasconcelos
Keyword(s):  
Surrogate Modeling ◽  
Electromagnetic Device ◽  
Modeling Techniques

scholarly journals A Comparison of Surrogate Modeling Techniques for Global Sensitivity Analysis in Hybrid Simulation

2021 ◽  
Vol 4 (1) ◽  
pp. 1-21
Author(s):  
Nikolaos Tsokanas ◽  
Roland Pastorino ◽  
Božidar Stojadinović
Keyword(s):  
Sensitivity Analysis ◽  
Hybrid Model ◽  
Polynomial Chaos ◽  
Global Sensitivity Analysis ◽  
Hybrid Simulation ◽  
Surrogate Modeling ◽  
Sobol Indices ◽  
Global Sensitivity ◽  
Model Response ◽  
Modeling Techniques

Hybrid simulation is a method used to investigate the dynamic response of a system subjected to a realistic loading scenario. The system under consideration is divided into multiple individual substructures, out of which one or more are tested physically, whereas the remaining are simulated numerically. The coupling of all substructures forms the so-called hybrid model. Although hybrid simulation is extensively used across various engineering disciplines, it is often the case that the hybrid model and related excitation are conceived as being deterministic. However, associated uncertainties are present, whilst simulation deviation, due to their presence, could be significant. In this regard, global sensitivity analysis based on Sobol’ indices can be used to determine the sensitivity of the hybrid model response due to the presence of the associated uncertainties. Nonetheless, estimation of the Sobol’ sensitivity indices requires an unaffordable amount of hybrid simulation evaluations. Therefore, surrogate modeling techniques using machine learning data-driven regression are utilized to alleviate this burden. This study extends the current global sensitivity analysis practices in hybrid simulation by employing various different surrogate modeling methodologies as well as providing comparative results. In particular, polynomial chaos expansion, Kriging and polynomial chaos Kriging are used. A case study encompassing a virtual hybrid model is employed, and hybrid model response quantities of interest are selected. Their respective surrogates are developed, using all three aforementioned techniques. The Sobol’ indices obtained utilizing each examined surrogate are compared with each other, and the results highlight potential deviations when different surrogates are used.

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