Modeling Dynamical Systems

The course intends to train the students in the art of computational modeling, by implementing dynamical systems models. The students will be introduced to modeling through various examples and practical sessions, outlining design principles and computational requirements. Students will learn to analyze models with the concepts and tools of dynamical systems theory, such as attractors, bifurcations and parameter space analysis. The course starts with the analysis of one-dimensional dynamical systems (such as the logistic map), moving on to multiple dimensions (Lotka-Volterra and neuronal network models) and finally to spatial models, such as coupled oscillators. At the end of the course students will use 3 sessions to implement and analyze their own model of choice. They then present this model and write a report about it.

1. The assessment consists of:

• a peer reviewed VIDEO Presentation on the final project (35%)

• peer reviewed group project (35%) -- delivered as a jupyter notebook

• 6 x weekly pass and fail assignments (30%).

2. The assignment resit consists of submitting all weekly deliverables and a video explainer and a Q&A session on the content.

3.a The deadline for the assignment resit is Q1.10

4. Registration for assessment is based on enrolling in the Brightspace page of the course.

6. Students needing to turn work in late due to personal circumstances must contact the Academic Counsellor prior to turning it in to make arrangements. Other late work might not be graded.