Learning objectives
Knowledge and understanding:
-Theoretical knowledge of linear and nonlinear equations governing pulse propagation in waveguide modes.
-Basic understanding of finite-difference modeling tools for guided-mode structure and Fourier-transform method for pulse propagation modeling.
-Basic skills in using the Python programming environment.
Applying knowledge and understanding:
-Modeling linear spatial field evolution in fibers.
-Modeling spectral and spatial field evolution induced by nonlinear optical effects in various fiber types.
Prerequisites
The student must have basic knowledge of wave and light propagation concepts.
Course unit content
Introduction to the Python programming environment. Wave equations in optical fibers, finite-difference approach. Step-index and parabolic-index fibers, exact solutions. Structure and modal properties of photonic crystal fibers. Mode types, scalar, vectorial, orbital angular momentum states. Linear field evolution in step-index fibers. Single- and multi-mode generalized nonlinear Schrödinger equation for nonlinear pulse propagation. Raman and four-wave mixing effects in single- and multi-mode fibers. Single- and multi-mode optical solitons. Soliton compression and fission, dispersive-wave generation in multi-mode fibers.
Full programme
Introduction to the Python programming environment. Wave equations in optical fibers, finite-difference approach. Step-index and parabolic-index fibers, exact solutions. Structure and modal properties of photonic crystal fibers. Mode types, scalar, vectorial, orbital angular momentum states. Linear field evolution in step-index fibers. Single- and multi-mode generalized nonlinear Schrödinger equation for nonlinear pulse propagation. Raman and four-wave mixing effects in single- and multi-mode fibers. Single- and multi-mode optical solitons. Soliton compression and fission, dispersive-wave generation in multi-mode fibers.
Bibliography
Lecture notes will be distributed before/during the course.
Teaching methods
The course will have roughly 30% lectures and 70% exercise work that can be done by the students under the teacher supervision.
Exercise work is intended to deeper understand and to experiment concepts and knowledge acquired during the lectures.
Assessment methods and criteria
Weekly/bi-weekly reports. Oral defense of reports at the end of the course.
The weekly reports should be brief and mostly contain answers to exercises solved during and in between the teaching sessions, along with appropriate discussion.
Feedback on these reports will be given continuously.
At the oral defense the student will randomly pick a topic, provide a short discussion of the relevant theory, and present the results of his/her report on this topic, answering critical questions from the examinators at the end.
Other information
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