TY - GEN
T1 - Comparative study of Machine Learning methods for variability analysis in High-speed link
AU - Nguyen, Thong
AU - Shi, Bobi
AU - Schutt-Aine, Jose
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/10
Y1 - 2021/5/10
N2 - Non-intrusive stochastic analysis of a complex system requires a fast deterministic solver to simulate the mapping between the input and output. Different machine learning methods, namely Partial Least Square regression, Gaussian Process, and Polynomial Chaos expansion can be used to represent the input-output mapping. Once they are trained to learn the mapping, they are used to replace the expensive process that generates the output given an input, such as a full-wave electrogmanetic solver. Aforementioned methods are compared in this paper when trained on a simple high-speed link.
AB - Non-intrusive stochastic analysis of a complex system requires a fast deterministic solver to simulate the mapping between the input and output. Different machine learning methods, namely Partial Least Square regression, Gaussian Process, and Polynomial Chaos expansion can be used to represent the input-output mapping. Once they are trained to learn the mapping, they are used to replace the expensive process that generates the output given an input, such as a full-wave electrogmanetic solver. Aforementioned methods are compared in this paper when trained on a simple high-speed link.
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U2 - 10.1109/SPI52361.2021.9505215
DO - 10.1109/SPI52361.2021.9505215
M3 - Conference contribution
AN - SCOPUS:85114950617
T3 - SPI 2021 - 25th IEEE Workshop on Signal and Power Integrity
BT - SPI 2021 - 25th IEEE Workshop on Signal and Power Integrity
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE Workshop on Signal and Power Integrity, SPI 2021
Y2 - 10 May 2021 through 12 May 2021
ER -