Journal article
Surila Guglani, Sourajeet Roy
Workshop on Signal Propagation on Interconnects, 2020
Workshop on Signal Propagation on Interconnects, 2020
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APA
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Guglani, S., & Roy, S. (2020). Development of Improved Predictor for Expedited Training of Polynomial Chaos Metamodels of Multi-Walled Carbon Nanotube Interconnects. Workshop on Signal Propagation on Interconnects.
Chicago/Turabian
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Guglani, Surila, and Sourajeet Roy. “Development of Improved Predictor for Expedited Training of Polynomial Chaos Metamodels of Multi-Walled Carbon Nanotube Interconnects.” Workshop on Signal Propagation on Interconnects (2020).
MLA
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Guglani, Surila, and Sourajeet Roy. “Development of Improved Predictor for Expedited Training of Polynomial Chaos Metamodels of Multi-Walled Carbon Nanotube Interconnects.” Workshop on Signal Propagation on Interconnects, 2020.
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@article{surila2020a,
title = {Development of Improved Predictor for Expedited Training of Polynomial Chaos Metamodels of Multi-Walled Carbon Nanotube Interconnects},
year = {2020},
journal = {Workshop on Signal Propagation on Interconnects},
author = {Guglani, Surila and Roy, Sourajeet}
}
Abstract
This paper presents an improved predictor-corrector algorithm to efficiently train polynomial chaos (PC) metamodels for the variability analysis of multiwalled carbon nanotube (MWCNT) interconnect networks. The salient feature of the proposed algorithm is the development of a more accurate predictor that can accelerate the convergence of conventional predictor-corrector algorithms. Therefore, the proposed predictor-corrector algorithm offers much greater speedup than conventional predictor-corrector algorithms when training PC metamodels.