التفاصيل البيبلوغرافية
| العنوان: |
LAR-PCE based stochastic kriging for non parametric problems. |
| المؤلفون: |
García-Merino, J. C., García-Macías, E., Calvo-Jurado, C. |
| المصدر: |
AIP Conference Proceedings; 2024, Vol. 3094 Issue 1, p1-4, 4p |
| مصطلحات موضوعية: |
KRIGING, POLYNOMIAL chaos, STOCHASTIC models, RESEARCH institutes |
| مستخلص: |
This research centers on the development of a metamodeling approach that extends the principles of stochastic kriging (SK) to model stochastic simulations. Surrogate modeling has opened up in fresh opportunities for addressing the constraints associated with computationally demanding numerical models. However, it is very difficult to ensure the accuracy of uncertainty quantification, especially due to measuring variability intrinsic to the stochastic simulation. To this aim, SK was established to characterize both sampling and response-surface uncertainties, respectively. In this work, a Polynomial Chaos Expansion based Stochastic Kriging (PC-SK) is proposed, as an alternative to the ordinary SK metamodel. The presented stochastic metamodel integrates adaptive sparse PC and Kriging meta-modelling to achieve predictive capabilities at both the global and local levels. Least Angle Regression (LAR) algorithm to develop the PC has been considered. The choice of an optimal sparse polynomial basis through LAR is crucial for obtaining good fits. The computational efficiency and accuracy of the proposed method is validated through different metrics and benchmarked against established deterministic and stochastic schemes. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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