Modified knowledge-based neural networks using control variates for the fast uncertainty quantification of on-chip MWCNT interconnects
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 …
developed for the efficient uncertainty quantification of on-chip multiwalled carbon nanotube …
Artificial Neural Networks With Fast Transfer Learning for Statistical Signal Integrity Analysis of MWCNT and MLGNR Interconnect Networks
In this article, artificial neural network (ANN) metamodels have been developed for the fast
statistical signal integrity analysis of multiwalled carbon nanotube and multilayer graphene …
statistical signal integrity analysis of multiwalled carbon nanotube and multilayer graphene …
Conservative Gaussian Process Models for Uncertainty Quantification and Bayesian Optimization in Signal Integrity Applications
P Manfredi - IEEE Transactions on Components, Packaging …, 2024 - ieeexplore.ieee.org
Surrogate modeling is being increasingly adopted in signal and power integrity analysis to
assist design exploration, optimization, and uncertainty quantification tasks. In this scenario …
assist design exploration, optimization, and uncertainty quantification tasks. In this scenario …
Combining Prior Knowledge with Transfer Learning (PKID-TL) for Fast Neural Network Enabled Uncertainty Quantification of Graphene On-Chip Interconnects
In this paper, an artificial neural network (ANN) enabled uncertainty quantification technique
is developed for graphene on-chip interconnects. In the proposed technique, primary ANNs …
is developed for graphene on-chip interconnects. In the proposed technique, primary ANNs …