Uncertainty quantification analysis using multi-fidelity deep neural network
Year: 2024
Type: conference-paper
Abstract: In order to solve the highly-dimensional and highly-nonlinear problems in uncertainty quantification of CFD, the paper develops a multi-fidelity neural network model and proposes a residual connection-based multi-fidelity deep neural network fusion model. Based on the small sample size and low computational cost of multi-fidelity models, the residual structure is further utilized to accelerate the convergence rate of the model and reduce the possibility of inducing systemic risks. Finally, the feasibility and accuracy of the algorithm are verified by six typical numerical examples, and the uncertainty quantification analysis of the NACA0012 airfoil’s yaw moment coefficients is completed.
Authors: Luogeng Lv, Jiao Zhao, Jiangtao Chen
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4405170813