{"title":"基于协同克里格的梁振动多保真度不确定性量化,采用粗、细有限元网格","authors":"R. Rohit, R. Ganguli","doi":"10.1080/15502287.2021.1921883","DOIUrl":null,"url":null,"abstract":"Abstract Multi-fidelity models have exploded in popularity as they promise to circumvent the computational complexity of a high-fidelity model without sacrificing accuracy. In this paper, we demonstrate the process of building a multi-fidelity model and illustrate its advantage through an uncertainty quantification study using the beam vibration problem. A multi-fidelity co-kriging model is built with data from low- and high-fidelity models, which are finite element models with coarse and fine discretization, respectively. The co-kriging model’s predictive capabilities are excellent, achieving accuracy within 1% of the high-fidelity model while providing 98% computational savings over the high-fidelity model in the uncertainty quantification study.","PeriodicalId":315058,"journal":{"name":"International Journal for Computational Methods in Engineering Science and Mechanics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Co-kriging based multi-fidelity uncertainty quantification of beam vibration using coarse and fine finite element meshes\",\"authors\":\"R. Rohit, R. Ganguli\",\"doi\":\"10.1080/15502287.2021.1921883\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Multi-fidelity models have exploded in popularity as they promise to circumvent the computational complexity of a high-fidelity model without sacrificing accuracy. In this paper, we demonstrate the process of building a multi-fidelity model and illustrate its advantage through an uncertainty quantification study using the beam vibration problem. A multi-fidelity co-kriging model is built with data from low- and high-fidelity models, which are finite element models with coarse and fine discretization, respectively. The co-kriging model’s predictive capabilities are excellent, achieving accuracy within 1% of the high-fidelity model while providing 98% computational savings over the high-fidelity model in the uncertainty quantification study.\",\"PeriodicalId\":315058,\"journal\":{\"name\":\"International Journal for Computational Methods in Engineering Science and Mechanics\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal for Computational Methods in Engineering Science and Mechanics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/15502287.2021.1921883\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal for Computational Methods in Engineering Science and Mechanics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/15502287.2021.1921883","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Co-kriging based multi-fidelity uncertainty quantification of beam vibration using coarse and fine finite element meshes
Abstract Multi-fidelity models have exploded in popularity as they promise to circumvent the computational complexity of a high-fidelity model without sacrificing accuracy. In this paper, we demonstrate the process of building a multi-fidelity model and illustrate its advantage through an uncertainty quantification study using the beam vibration problem. A multi-fidelity co-kriging model is built with data from low- and high-fidelity models, which are finite element models with coarse and fine discretization, respectively. The co-kriging model’s predictive capabilities are excellent, achieving accuracy within 1% of the high-fidelity model while providing 98% computational savings over the high-fidelity model in the uncertainty quantification study.