How the Course Works

Module 1: Low-Dimensional Dynamics

This page summarizes the organization and assessment of the course. The syllabus is the reference document; if anything here differs from it, the syllabus applies.

Lectures and laboratories

The course has 40 sessions: 18 lectures, 12 computer laboratory sessions, 5 sessions for the group project and 5 for assessment. Lectures and labs alternate. A lecture introduces a model and its analysis; the next lab implements, simulates and visualizes it in Python. Simulations built in a lab are reused in later lectures.

The course is organized in seven parts:

Part I, low-dimensional deterministic dynamics (this module).
Part II, synchronization and collective behaviour.
Part III, modelling with randomness.
Part IV, space and pattern formation.
Part V, complex networks.
Part VI, explorables and discrete models.
Part VII, assessment and group project.

Assessment

Item	Weight	Format
Assignments	20%	Two hand-in assignments, 10% each: the Kuramoto model and pattern formation
Explorables presentation	10%	Presentation in pairs of a model from the Complexity Explorables collection
Exam	25%	Individual, multiple choice, after the course material has been covered
Group project	45%	Groups of three: written report, poster, short video and oral defence

You need at least 3.5 in the individual exam to pass the course.

The two graded assignments are:

Pages called “practice” in this module are ungraded. Use them to prepare for the assignments and the project.

Use of generative AI

You may use generative AI tools as a learning aid and as a coding aid, and you should acknowledge that use. You may not use them to solve homework assignments or during the exam. The syllabus gives the full policy and a suggested acknowledgement format.

What you need

Python with the packages in requirements.txt of the course repository. See Reproducible Code.
The compulsory textbook, Strogatz (2024). Each session page lists its readings in a callout at the top.

Session 2 builds the first model of the course.

References

Strogatz, Steven H. 2024. Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering. Chapman; Hall/CRC.

--- title: "How the Course Works" subtitle: "Module 1: Low-Dimensional Dynamics" format: html --- This page summarizes the organization and assessment of the course. The [syllabus](../../syllabus.qmd) is the reference document; if anything here differs from it, the syllabus applies. ## Lectures and laboratories The course has 40 sessions: 18 lectures, 12 computer laboratory sessions, 5 sessions for the group project and 5 for assessment. Lectures and labs alternate. A lecture introduces a model and its analysis; the next lab implements, simulates and visualizes it in Python. Simulations built in a lab are reused in later lectures. The course is organized in seven parts: - Part I, low-dimensional deterministic dynamics (this module). - Part II, synchronization and collective behaviour. - Part III, modelling with randomness. - Part IV, space and pattern formation. - Part V, complex networks. - Part VI, explorables and discrete models. - Part VII, assessment and group project. ## Assessment | Item | Weight | Format | |---|---|---| | Assignments | 20% | Two hand-in assignments, 10% each: the Kuramoto model and pattern formation | | Explorables presentation | 10% | Presentation in pairs of a model from the Complexity Explorables collection | | Exam | 25% | Individual, multiple choice, after the course material has been covered | | Group project | 45% | Groups of three: written report, poster, short video and oral defence | You need at least 3.5 in the individual exam to pass the course. The two graded assignments are: - [Kuramoto assignment](../collective-behaviour/assignment-kuramoto.qmd){.btn .btn-secondary} - [Pattern formation assignment](../pattern-formation/assignment-patterns.qmd){.btn .btn-secondary} Pages called "practice" in this module are ungraded. Use them to prepare for the assignments and the project. ## Use of generative AI You may use generative AI tools as a learning aid and as a coding aid, and you should acknowledge that use. You may not use them to solve homework assignments or during the exam. The syllabus gives the full policy and a suggested acknowledgement format. ## What you need - Python with the packages in `requirements.txt` of the [course repository](https://github.com/daniprec/BAM-Applied-Math-Lab). See [Reproducible Code](reproducibility.qmd). - The compulsory textbook, @strogatz2024nonlinear. Each session page lists its readings in a callout at the top. ## Next Session 2 builds the first model of the course.