Structural uncertainty quantification with partial information
作者：
Highlights：
• Merging data science and soft computing within the big data analysis.
• Uncertainty quantification in the presence of aleatory and epistemic randomness.
• Matrix completion with partial initial data.
• Incorporating clustering and regression in matrix completion task.
• Developing surrogate-assisted uncertainty quantification for critical-mission infrastructures.
摘要
•Merging data science and soft computing within the big data analysis.•Uncertainty quantification in the presence of aleatory and epistemic randomness.•Matrix completion with partial initial data.•Incorporating clustering and regression in matrix completion task.•Developing surrogate-assisted uncertainty quantification for critical-mission infrastructures.
论文关键词：Uncertainty quantification,Numerical simulation,Aleatory,Epistemic,Matrix completion
论文评审过程：Received 8 July 2021, Revised 22 February 2022, Accepted 22 February 2022, Available online 9 March 2022, Version of Record 24 March 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116736
