Uncertainty Quantification of Electromagnetic Devices, Circuits, and Systems

Contents
About the editor
1. Uncertainty quantification in electromagnetic device, circuit, and system simulation: its importance and value | Sourajeet Roy
1.1 Basics of numerical simulation
1.2 Deterministic numerical simulation
1.3 Stochastic numerical simulation
1.4 State-of-the-art in performing uncertainty quantification
1.5 Organization of the book
1.6 Conclusion
References
2. Polynomial chaos based uncertainty quantification in electrical engineering: theory | Paolo Manfredi and Dries Vande Ginste
2.1 The polynomial chaos expansion
2.2 Non-intrusive methods
2.3 The SG method
2.4 Advanced methods
References
3. Polynomial chaos based uncertainty quantification in electrical engineering: applications | Paolo Manfredi and Dries Vande Ginste
3.1 Electrical circuits
3.2 Multiconductor transmission lines
3.3 Full-wave electromagnetic application examples
References
4. Dimension reduction strategies to address the curse of dimensionality in polynomial chaos | Sourajeet Roy
4.1 Introduction
4.2 Global sensitivity analyses
4.3 Sensitivity analysis via HDMR
4.4 Illustrative examples
4.5 Conclusion
References
5. A predictor–corrector algorithm for fast polynomial chaos based statistical modeling of carbon nanotube interconnects | Surila Guglani and Sourajeet Roy
5.1 Introduction
5.2 State-of-the-art in modeling MWCNT networks
5.3 Predictor–corrector algorithm for MWCNT networks
5.4 Illustrative examples
5.5 Improved predictor–corrector algorithms
5.6 Conclusion
References
6. Uncertainty quantification and design optimization with non-Gaussian correlated process variations | Zheng Zhang
6.1 Background: gPC-type methods
6.2 Basis functions with non-Gaussian correlations
6.3 Toward a stochastic collocation framework
6.4 Yield-aware chance-constraint design optimization
6.5 Conclusions
Acknowledgements
References
7. Machine learning approaches towards uncertainty quantification | Riccardo Trinchero and Flavio Canavero
7.1 What is a surrogate model?
7.2 Learning paradigm
7.3 Ordinary least squares
7.4 Bias-variance trade-off
7.5 Regularizer
7.6 Tuning of the Hyperparameter
7.7 Summary
7.8 Machine learning based metamodels
7.9 LS-SVM regression
7.10 Gaussian process regression
7.11 Application examples
7.12 From single-output static systems to multi output dynamic systems
7.13 PCA-compressed surrogate modeling
7.14 Application example: 16-bit flash memory bus
References
8. Artificial neural network-based yield optimization with uncertainties in EM structural parameters | Feng Feng, Jianan Zhang and Qi-Jun Zhang
8.1 Introduction
8.2 The MC-based approach to yield-driven EM optimization
8.3 The SM-based approach to yield-driven EM optimization
8.4 The PC-based approach to yield-driven EM optimization
8.5 The ANN-based approach to yield-driven EM optimization
8.6 Discussion
8.7 Conclusion
References
9. Exploring order reduction clustering methods for uncertainty quantification of electromagnetic composite structures | Sebastien Lallechere
9.1 Introduction
9.2 Statement of the problem
9.3 Assessing stochastic shielding properties with MC and SROM
9.4 Conclusion
References
10. Mixed epistemic-aleatory uncertainty using a new polynomial chaos formulation combined with machine learning | Domenico Spina, Tom Dhaene and Flavia Grassi
10.1 Introduction
10.2 UQ with epistemic variables
10.3 Characterization of hybrid epistemic-aleatory UQ and UP problems
10.4 Conclusions
References
11. Conclusions and future directions | Sourajeet Roy
11.1 Overview of the chapters in this book
11.2 State-of-the-art in uncertainty quantification
11.3 Key challenges facing uncertainty quantification
References
Index