EE 2005 - Electromagnetic Field Theory

The objective of this course is to provide students with fundamental knowledge of electromagnetic field theory. The course covers vector analysis, electrostatic fields, magnetostatic fields, Maxwell’s equations, and fundamental engineering concepts related to electromagnetic fields.The course also aims to develop students’ ability to model, analyze, and interpret electromagnetic field problems using analytical methods. In addition, the course provides awareness of basic computational and simulation approaches used in electromagnetic system analysis.

Introduction to electromagnetic theory, coordinate systems and vector analysis, scalar and vector fields, gradient, divergence, and curl operations, Coulomb’s law, Gauss’s law, electrostatic fields and potential, capacitance, magnetic fields, Biot–Savart law, Ampere’s law, magnetic force and torque, inductance, magnetic materials, time-varying electromagnetic fields, and Maxwell’s equations are covered. Analytical modeling and solution of fundamental electromagnetic problems are also included throughout the course.

PHYS 1002, MATH 1001 (Pre-requisites)

Course Textbook: Fawwaz T. Ulaby, Umberto Ravaioli, Fundamentals of Applied Electromagnetics, 7th Edition, Pearson Education, 2015. Supplementary References: 1) David K. Cheng, Field and Wave Electromagnetics, Pearson. 2) William H. Hayt, John A. Buck, Engineering Electromagnetics, McGraw-Hill. 3) Matthew N.O. Sadiku, Elements of Electromagnetics, Oxford University Press. Course Materials: Lecture notes and presentations, MATLAB/Python-based introductory electromagnetic field analysis applications, Electromagnetic field visualization and simulation examples, Problem-solving and application documents.

Full compliance with academic integrity and ethical principles is expected in this course. Plagiarism, cheating, unauthorized citation, and all other forms of unethical behavior are strictly prohibited in assignments, projects, reports, examinations, and all academic work. The use of artificial intelligence-based tools (such as ChatGPT, Copilot, Gemini, etc.) is strongly encouraged to accelerate the learning process, improve research efficiency, support coding activities, and enhance the quality of academic work. However, these tools should only be considered as supportive tools. Students are expected to analyze, verify, improve, and critically evaluate the content generated by AI tools and to incorporate their own academic contributions into their work. Submitting AI-generated content directly and verbatim without original student contribution will be considered a violation of academic integrity and may be treated as plagiarism. When deemed necessary, students may be asked to verbally explain the details of their submitted work, code, or reports and technically defend the methods they used.