Uncertainty Quantification of Electromagnetic Devices, Circuits, and Systems describes the advances made over the last decade in the topic of uncertainty quantification (UQ) and stochastic analysis. The primary goal of the book is to educate and inform electronics engineers about the most recent numerical techniques, mathematical theories, and computational methods to perform UQ for electromagnetic devices, circuits, and systems.

• Chapter 1: Uncertainty quantification in electromagnetic device, circuit, and system simulation: its importance and value
• Chapter 2: Polynomial chaos based uncertainty quantification in electrical engineering: theory
• Chapter 3: Polynomial chaos based uncertainty quantification in electrical engineering: applications
• Chapter 4: Dimension reduction strategies to address the curse of dimensionality in polynomial chaos
• Chapter 5: A predictor-corrector algorithm for fast polynomial chaos based statistical modeling of carbon nanotube interconnects
• Chapter 6: Uncertainty quantification and design optimization with non-Gaussian correlated process variations
• Chapter 7: Machine learning approaches towards uncertainty quantification
• Chapter 8: Artificial neural network-based yield optimization with uncertainties in EM structural parameters
• Chapter 9: Exploring order reduction clustering methods for uncertainty quantification of electromagnetic composite structures
• Chapter 10: Mixed epistemic-aleatory uncertainty using a new polynomial chaos formulation combined with machine learning
• Chapter 11: Conclusions and future directions