Version 1
: Received: 26 April 2021 / Approved: 28 April 2021 / Online: 28 April 2021 (17:27:43 CEST)
Version 2
: Received: 23 June 2021 / Approved: 25 June 2021 / Online: 25 June 2021 (11:58:24 CEST)
Version 3
: Received: 22 January 2022 / Approved: 24 January 2022 / Online: 24 January 2022 (10:39:52 CET)
How to cite:
Cao, J.; Li, Q.; Xu, L.; Yang, R.; Dai, Y. Uncertainty Quantification And Sensitivity Analysis Of Parameterization-free Surrogate Model. Preprints2021, 2021040762. https://doi.org/10.20944/preprints202104.0762.v3
Cao, J.; Li, Q.; Xu, L.; Yang, R.; Dai, Y. Uncertainty Quantification And Sensitivity Analysis Of Parameterization-free Surrogate Model. Preprints 2021, 2021040762. https://doi.org/10.20944/preprints202104.0762.v3
Cao, J.; Li, Q.; Xu, L.; Yang, R.; Dai, Y. Uncertainty Quantification And Sensitivity Analysis Of Parameterization-free Surrogate Model. Preprints2021, 2021040762. https://doi.org/10.20944/preprints202104.0762.v3
APA Style
Cao, J., Li, Q., Xu, L., Yang, R., & Dai, Y. (2022). Uncertainty Quantification And Sensitivity Analysis Of Parameterization-free Surrogate Model. Preprints. https://doi.org/10.20944/preprints202104.0762.v3
Chicago/Turabian Style
Cao, J., Rui Yang and Yuejin Dai. 2022 "Uncertainty Quantification And Sensitivity Analysis Of Parameterization-free Surrogate Model" Preprints. https://doi.org/10.20944/preprints202104.0762.v3
Abstract
Surrogate model based optimization method is widely-used to accelerate the design and optimization process~\cite{marler2004survey}. The input of regression model used in the surrogate model are numbers, which requires users to parametrize the geometries. In this paper, a new parameterization-free surrogate model is introduced and its corresponding uncertainty quantification and sensitivity analysis method are discussed. The input of new surrogate model methods is surface mesh of simulation domain. \Gls{gnns} is used to extract geometric information, and \Gls{cnns} is used to predict contours. This framework bypasses parameterization,as a consequence, uncertainties introduced by manual parameterization is reduced. However, such changes compared with conventional surrogate model methods impose great challenge on uncertainties quantification and sensitivity analysis. Uncertainties quantification in this paper means the error bar of prediction results, which is calculated by Gaussian Process Regression method in current surrogate method. In this paper, a new quantification method achieved by Kullback-Leibler divergence (KLD) is introduced. And the sensitivity analysis is conducted by Automatic Differentiation, which gives a Jacobian matrix of inputs. The method and analysis mentioned above are demonstrated by a low-pressure steam turbine rotator and its exhaust system.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Jiajun Cao
Commenter's Conflict of Interests: Author