Introduction to Computational Science

Course manual 2025/2026

Course content

An introduction to modelling and simulation and the 3rd paradigm of science. You learn to use systems of ordinary differential equations,  networks and individual-based models to describe real-world systems. You will learn how to apply analytical techniques (e.g., solving ODEs, non-linear dynamics, stability analysis) and simulation techniques (numerical integration, network simulations) to apply the models.  The course focuses on the methods, but does this through the example of epidemics and in particular the SIR model.

Study materials

Literature

• Modeling Infectious Diseases in Humans and Animals

• https://networksciencebook.com/

• Non-linear dynamics and chaos

   

Other

• Lecture notes, hand outs, on-line material.

Objectives

• The aim of this course is to provide an overview of Computational Science and modelling techniques available to the Computational Scientist
• The student will be able to describe and apply the basic concepts of modelling and simulation (Validation, Verification, Experimental Frame, etc.)
• The student will be able to design and analyse ODEs as an approach to understand and model real world systems (particularly epidemics)
• The student will be able to apply methods of network science to model real world phenomena.
• The student will be able to compare modelling approaches and understand under which conditions each approach is most appropriate.
• The student will build a basic understanding of epidemics and be able to understand the factors in epidemics that make them challenging to model.
• The student will be able to implement and analyse computer simulations using Python (and associated libraries)

Teaching methods

• Lecture
• Computer lab session/practical training

Attendance

This programme does not have requirements concerning attendance (Ter part B).

Assessment

Inspection of assessed work

The manner of inspection will be communicated via the digitial learning environment.

Assignments

Assignment 1: SIR model (ODEs)

• The SIR model is an epidemiological model that computes the spread of an infectious disease through a population of people. It is used to compute the fraction of susceptible (S), infected (I), and recovered (R) individuals at any given time through the spread of an infectious disease.

Assignment 2: Stochastic and Spatial Models

• In this assignment you (in teams of two people) will be exploring other ways to model infectious diseases. In the first part of the assignment you will use a stochastic discrete event model to compute the spread of an infectious disease through a population. And in the second half of the assignment you will explore spatial models (in particular networks)

  to study the spread of infectious diseases.

The assignments are graded following this weighting scheme:

30% -- Quality of the report (introduction, background/theory, experimental method, discussion, references)

55% -- Content (Answers the topics presented in the assignment)

15% -- Code (Does the code reproduce the results presented in the report)

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Additional information

Prerequisites: Academic Bachelor in one of the sciences.

Contact information

Coordinator

• dr. Valeria Krzhizhanovskaya