Uncertainty Quantification of Electromagnetic Devices, Circuits, and Systems

Importantly, the book offers an in-depth exploration of the recent explosion in surrogate modelling (metamodeling) techniques for numerically efficient UQ. Metamodeling has currently become the most attractive, numerically efficient, and popular approach for UQ.
The book begins by introducing the concept of uncertainty quantification in electromagnetic device, circuit, and system simulation. Further chapters cover the theory and applications of polynomial chaos based uncertainty quantification in electrical engineering; dimension reduction strategies to address the curse of dimensionality in polynomial chaos; a predictor-corrector algorithm for fast polynomial chaos based statistical modeling of carbon nanotube interconnects; machine learning approaches towards uncertainty quantification; artificial neural network-based yield optimization with uncertainties in EM structural parameters; exploring order reduction clustering methods for uncertainty quantification of electromagnetic composite structures; and mixed epistemic-aleatory uncertainty using a new polynomial chaos formulation combined with machine learning. A final chapter provides concluding remarks and explores potential future directions for research in the field.
The book will be a welcome resource for advanced students and researchers in electromagnetics and applied mathematical modelling who are working on electronic circuit and device design.

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