CMAS Lab - A Multi-Fidelity Polynomial Chaos Approach for Uncertainty

Journal article

Surila Guglani, K. Dimple, Brajesh Kumar Kaushik, Sourajeet Roy, Rohit Sharma
Workshop on Signal Propagation on Interconnects, 2021

Semantic Scholar DOI

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APA Click to copy
Guglani, S., Dimple, K., Kaushik, B. K., Roy, S., & Sharma, R. (2021). A Multi-Fidelity Polynomial Chaos Approach for Uncertainty Quantification of MWCNT Interconnect Networks in the Presence of Imperfect Contacts. Workshop on Signal Propagation on Interconnects.

Chicago/Turabian Click to copy
Guglani, Surila, K. Dimple, Brajesh Kumar Kaushik, Sourajeet Roy, and Rohit Sharma. “A Multi-Fidelity Polynomial Chaos Approach for Uncertainty Quantification of MWCNT Interconnect Networks in the Presence of Imperfect Contacts.” Workshop on Signal Propagation on Interconnects (2021).

MLA Click to copy
Guglani, Surila, et al. “A Multi-Fidelity Polynomial Chaos Approach for Uncertainty Quantification of MWCNT Interconnect Networks in the Presence of Imperfect Contacts.” Workshop on Signal Propagation on Interconnects, 2021.

BibTeX Click to copy

@article{surila2021a,
  title = {A Multi-Fidelity Polynomial Chaos Approach for Uncertainty Quantification of MWCNT Interconnect Networks in the Presence of Imperfect Contacts},
  year = {2021},
  journal = {Workshop on Signal Propagation on Interconnects},
  author = {Guglani, Surila and Dimple, K. and Kaushik, Brajesh Kumar and Roy, Sourajeet and Sharma, Rohit}
}

Abstract

In this paper, a polynomial chaos (PC) approach based on the concept of multi-fidelity algorithms is presented for uncertainty quantification of multi-walled carbon nanotube (MWCNT) interconnect networks exhibiting imperfect contacts. The salient feature of the proposed approach is the development of a new low-fidelity model where each MWCNT conductor is represented as multiple parasitically coupled equivalent conductors depending on the nature of the contact resistance of each shell making up that conductor. This proposed low-fidelity model is provably more accurate than existing low-fidelity models, thereby leading to even faster construction of PC metamodels than previously possible.