Uncertainty quantification of electromagnetic devices, circuits, and systems edited by Sourajeet Roy.
Material type:
Computer fileLanguage: English Publication details: London, UK : SciTech Publishing, 2022Description: 1 online resource (297, pages) : color illustrationsContent type: - text
- computer
- online resource
- 9781839531712 (e-book)
- QC760 Un1 2022
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Ladislao N. Diwa Memorial Library Multimedia Section | Non-fiction | OEBP QC760 Un1 2022 (Browse shelf(Opens below)) | Available | PAV | OEBP000369 | ||
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Ladislao N. Diwa Memorial Library Multimedia Section | Non-fiction | EB QC760 Un1 2022 (Browse shelf(Opens below)) | Room use only | PAV | EB000369 |
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| OEBP QC21 M72 2020 Modern physics / | OEBP QC30 B64 2021 General physics / | OEBP QC6 N485N48 2020 Thinking about physics / | OEBP QC760 Un1 2022 Uncertainty quantification of electromagnetic devices, circuits, and systems | OEBP QH332 B54 2021 General biology / | OEBP QH431 H36 2021 What makes you unique? : the secrets of genes and heredity / | OEBP QR49 An5 2020 Animal microbiology / |
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1. Uncertainty quantification in electromagnetic device, circuit, and system simulation: its importance and value, Sourajeet Roy -- 2. Polynomial chaos based uncertainty quantification in electrical engineering : theory, Paolo Manfredi and Dries Vande Ginste -- 3. Polynomial chaos based uncertainty quantification in electrical engineering: applications, Paolo Manfredi and Dries Vande Ginste -- 4. Dimension reduction strategies to address the curse of dimensionality in polynomial chaos, Sourajeet Roy -- 5. A predictor–corrector algorithm for fast polynomial chaos based statistical modeling of carbon nanotube interconnects, Surila Guglani and Sourajeet Roy -- 6. Uncertainty quantification and design optimization with non-Gaussian correlated process variations, Zheng Zhang -- 7. Machine learning approaches towards uncertainty quantification, Riccardo Trinchero and Flavio Canavero -- 8. Artificial neural network-based yield optimization with uncertainties in EM structural parameters, Feng Feng, Jianan Zhang and Qi-Jun Zhang -- 9. Exploring order reduction clustering methods for uncertainty quantification of electromagnetic composite structures, Sebastien Lallechere -- 10. Mixed epistemic-aleatory uncertainty using a new polynomial chaos formulation combined with machine learning, Domenico Spina, Tom Dhaene and Flavia Grassi -- 11. Conclusions and future directions, Sourajeet Roy
This book focuses on the advances made in the topic of uncertainty quantification (UQ) and stochastic analysis for the design of electromagnetic devices, circuits and systems. It covers the recent explosion in surrogate modelling (metamodeling) techniques for numerically efficient UQ - an attractive, efficient, and popular approach.
Fund 164 CE-Logic Purchased November 9, 2022 OEBP000369 P. Roderno PHP 14,672.75
2022-11-1010 2022-9-1288