2020/02/12 by Kai Zhou, Zhou, Kai, Jiong Tang +1
Computer Science · Decision Sciences · Engineering · Mathematics · Physics and Astronomy · #Advanced Multi-Objective Optimization Algorithms #Algorithm #Applications (stat.AP) #Artificial intelligence #Computer science #Data Analysis #Engineering #FOS: Computer and information sciences #FOS: Physical sciences #Fidelity #Gaussian #Gaussian process #High fidelity #Machine learning #Mathematics #Methodology (stat.ME) #Mode (computer interface) #Monte Carlo method #Probabilistic and Robust Engineering Design #Probabilistic logic #Process (computing) #Statistics #Statistics and Probability (physics.data-an) #Structural Health Monitoring Techniques #Uncertainty quantification #Variation (astronomy) #physics.data-an #stat.AP #stat.ME
paper · pdf · doi:10.48550/arxiv.2002.09287
arxiv created 2020/02/12 · openalex publication_date 2020/02/12 · arxiv updated 2020/02/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
Mode shape information play the essential role in deciding the spatial pattern of vibratory response of a structure. The uncertainty quantification of mode shape, i.e., predicting mode shape variation when the structure is subjected to uncertainty, can provide guidance for robust design and control. Nevertheless, computational efficiency is a challenging issue. Direct Monte Carlo simulation is unlikely to be feasible especially for a complex structure with large number of degrees of freedom. In this research, we develop a new probabilistic framework built upon Gaussian process meta-modeling architecture to analyze mode shape variation. To expedite the generation of input dataset for meta-model establishment, a multi-level strategy is adopted which can blend a large amount of low-fidelity data acquired from order-reduced analysis with a small amount of high-fidelity data produced by high-dimensional full finite element analysis. To take advantage of the intrinsic relation of spatial distribution of mode shape, a multi-response strategy is incorporated to predict mode shape variation at different locations simultaneously. These yield a multi-level, multi-response Gaussian process that can efficiently and accurately quantify the effect of structural uncertainty to mode shape variation. Comprehensive case studies are carried out for demonstration and validation.