Modulus prediction of buckypaper based on multi-fidelity analysis involving latent variables

Arash Pourhabib, Jianhua Z. Huang, Kan Wang, Chuck Zhang, Ben Wang, Yu Ding

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Buckypapers are thin sheets produced from Carbon NanoTubes (CNTs) that effectively transfer the exceptional mechanical properties of CNTs to bulk materials. To accomplish a sensible tradeoff between effectiveness and efficiency in predicting the mechanical properties of CNT buckypapers, a multi-fidelity analysis appears necessary, combining costly but high-fidelity physical experiment outputs with affordable but low-fidelity Finite Element Analysis (FEA)-based simulation responses. Unlike the existing multi-fidelity analysis reported in the literature, not all of the input variables in the FEA simulation code are observable in the physical experiments; the unobservable ones are the latent variables in our multi-fidelity analysis. This article presents a formulation for multi-fidelity analysis problems involving latent variables and further develops a solution procedure based on nonlinear optimization. In a broad sense, this latent variable-involved multi-fidelity analysis falls under the category of non-isometric matching problems. The performance of the proposed method is compared with both a single-fidelity analysis and the existing multi-fidelity analysis without considering latent variables, and the superiority of the new method is demonstrated, especially when we perform extrapolation.
Original languageEnglish (US)
Pages (from-to)141-152
Number of pages12
JournalIIE Transactions
Issue number2
StatePublished - 2015
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2021-10-08
Acknowledged KAUST grant number(s): KUSCI-016-04
Acknowledgements: The authors would like to acknowledge the generous support from their sponsors. Ding and Pourhabib are partially supported by NSF under grant no. CMMI-1000088; Ding and Huang are partially supported by AFOSR DDDAS program under grant no. FA9550-13-1-0075 and King Abdullah University of Science and Technology award KUSCI-016-04; Huang is partially supported by NSF under grant no. DMS-1208952.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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