Nonlinear dynamics is relevant in the fields of brain dynamics, population and social dynamics, mathematical biology, physics, electrical engineering, and many more. Recently, machine learning methods have been applied to nonlinear dynamics.
The course covers the fundamental concepts and tools for solving systems involving nonlinear dynamics. Emphasis is on the qualitative graphical outlining of the solutions for the problems whose detailed analytical solutions are typically overwhelming or downright impossible. Still, even this level of solving requires some familiarity with differential equations and linear algebra. We will cover the material and do the exercises at a pace that gives everyone the chance to brush up the required pieces of mathematics.
The Course book is Steven Strogatz: Nonlinear Dynamics and Chaos can be found under Materials.
Solutions to the weekly returned exercises will be uploaded after exercise sessions.
Lecturer: Riku Linna
Assistant: Abbas KarimiLearning Outcomes
After completing the course the student will have an understanding of the basic classes of nonlinear systems and will be able to analyse them using analytic and diagrammatic methods. Based on these skills he/she will be able to solve these systems also numerically (although numerical methods will be in minor role in this course). The student will have an understanding of how and why a dynamical system becomes chaotic. He/she will understand fundamental characteristics of chaotic systems and how they are modelled.
Roughly first-year mathematics.
The grade (1-5) will be determined by an exam. Doing the assignments is voluntary but highly recommendable. 20% of lost exam points can be compensated by points gathered from assignments. See further details under Assignments. There will be 10 lectures and 10 rounds of assignments.