CMAS Lab - Modified Knowledge-Based Neural Networks Using Control

Journal article

K. Dimple, Surila Guglani, A. Dasgupta, Rohit Sharma, Sourajeet Roy, Brajesh Kumar Kaushik
IEEE transactions on electromagnetic compatibility (Print), 2023

Semantic Scholar DOI

Cite

APA Click to copy
Dimple, K., Guglani, S., Dasgupta, A., Sharma, R., Roy, S., & Kaushik, B. K. (2023). Modified Knowledge-Based Neural Networks Using Control Variates for the Fast Uncertainty Quantification of On-Chip MWCNT Interconnects. IEEE Transactions on Electromagnetic Compatibility (Print).

Chicago/Turabian Click to copy
Dimple, K., Surila Guglani, A. Dasgupta, Rohit Sharma, Sourajeet Roy, and Brajesh Kumar Kaushik. “Modified Knowledge-Based Neural Networks Using Control Variates for the Fast Uncertainty Quantification of On-Chip MWCNT Interconnects.” IEEE transactions on electromagnetic compatibility (Print) (2023).

MLA Click to copy
Dimple, K., et al. “Modified Knowledge-Based Neural Networks Using Control Variates for the Fast Uncertainty Quantification of On-Chip MWCNT Interconnects.” IEEE Transactions on Electromagnetic Compatibility (Print), 2023.

BibTeX Click to copy

@article{k2023a,
  title = {Modified Knowledge-Based Neural Networks Using Control Variates for the Fast Uncertainty Quantification of On-Chip MWCNT Interconnects},
  year = {2023},
  journal = {IEEE transactions on electromagnetic compatibility (Print)},
  author = {Dimple, K. and Guglani, Surila and Dasgupta, A. and Sharma, Rohit and Roy, Sourajeet and Kaushik, Brajesh Kumar}
}

Abstract

In this article, a modified knowledge-based artificial neural network (KBANN) metamodel is developed for the efficient uncertainty quantification of on-chip multiwalled carbon nanotube (MWCNT) interconnects. The proposed KBANN metamodel utilizes the notion of control variates to enable much faster training than what is possible with standard KBANNs. Importantly, techniques to calculate the optimal value of the control variates in an a priori manner without augmenting the training dataset have been developed in this article. Furthermore, techniques to exploit the control variates depending on whether one or multiple low-fidelity models of the MWCNT interconnects are available have also been developed in this article. The benefits of the proposed KBANN metamodel using control variates over standard KBANN metamodels have been validated using multiple MWCNT interconnect examples spanning multiple technology nodes.