This course explores electromagnetic phenomena in modern applications, including wireless and optical communications, circuits, computer interconnects and peripherals, microwave communications and radar, antennas, sensors, micro-electromechanical systems, and power generation and transmission. Fundamentals include quasistatic and dynamic solutions to Maxwell's equations; waves, radiation, and diffraction; coupling to media and structures; guided waves; resonance; acoustic analogs; and forces, power, and energy.
This course examines electric and magnetic quasistatic forms of Maxwell's equations applied to dielectric, conduction, and magnetization boundary value problems. Topics covered include: electromagnetic forces, force densities, and stress tensors, including magnetization and polarization; thermodynamics of electromagnetic fields, equations of motion, and energy conservation; applications to synchronous, induction, and commutator machines; sensors and transducers; microelectromechanical systems; propagation and stability of electromechanical waves; and charge transport phenomena. Acknowledgments The instructor would like to thank Thomas Larsen and Matthew Pegler for transcribing into LaTeX the homework problems, homework solutions, and exam solutions.
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A. Kumar, P. Liang, und T. Ma. (2019)cite arxiv:1909.10155Comment: Accepted as a spotlight to NeurIPS 2019, original title was "Variance Reduced Uncertainty Calibration".
O. Montasser, S. Hanneke, und N. Srebro. Proceedings of the Thirty-Second Conference on Learning Theory, Volume 99 von Proceedings of Machine Learning Research, Seite 2512--2530. Phoenix, USA, PMLR, (25--28 Jun 2019)
J. Hron, A. Matthews, und Z. Ghahramani. Proceedings of the 35th International Conference on Machine Learning, Volume 80 von Proceedings of Machine Learning Research, Seite 2019--2028. Stockholmsmässan, Stockholm Sweden, PMLR, (10--15 Jul 2018)
J. Huggins, M. Kasprzak, T. Campbell, und T. Broderick. (2019)cite arxiv:1910.04102Comment: A python package for carrying out our validated variational inference workflow -- including doing black-box variational inference and computing the bounds we develop in this paper -- is available at https://github.com/jhuggins/viabel. The same repository also contains code for reproducing all of our experiments.
M. Vadera, A. Cobb, B. Jalaian, und B. Marlin. (2020)cite arxiv:2007.04466Comment: Presented at the ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning.