Probabilistic Uncertainty Quantification of Microwave Circuits Using Gaussian Processes

• Published in: IEEE transactions on microwave theory and techniques Vol. 71; no. 6; pp. 1 - 13
• Main Author: Manfredi, Paolo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Amplifier design; Analytical models; Design parameters; Gaussian process; Gaussian process regression (GPR); Integrated circuit modeling; kriging; Machine learning; Mathematical models; Microwave circuits; Microwave theory and techniques; Polynomials; Principal component analysis; principal component analysis (PCA); Principal components analysis; Probabilistic logic; S parameters; Statistical analysis; surrogate modeling; Training; Uncertainty; uncertainty quantification (UQ)

In this article, a probabilistic machine learning framework based on Gaussian process regression (GPR) and principal component analysis (PCA) is proposed for the uncertainty quantification (UQ) of microwave circuits. As opposed to most surrogate modeling techniques, GPR models inherently carry infor...