URL study guide
https://tue.osiris-student.nl/onderwijscatalogus/extern/cursus?cursuscode=2DI66&collegejaar=2025&taal=enDescription
Many real-life processes are too difficult or too complex to analyse in an exact, theoretical way. Throughout this course we discuss a variety of real-life processes that exhibit a stochastic behaviour, ranging from financial models, manufacturing networks, to models for particle systems in chemistry or physics. The course is very practical, in the sense that emphasis will be placed on developing simulation models for these processes. The main goal of the course is that the students not only learn and understand the required simulation techniques, but are also forced to actually write the simulation. Moreover, self-motivation of the student is stimulated by not having classical lectures. Instead, most of the topics should be learned through self-study, reading the lecture notes, watching online instruction videos, and learning from the provided sample code. Students will get the opportunity in instructions to ask questions to the lecturer.The practical aspect is also reflected in the way that the assessment takes place: students, working together in (oftentimes multidisciplinary) groups, will be given several assignments. Each assignment addresses a real-life problem. It is not only important that the students are capable of finding a suitable model for the problem, and writing a program that simulates this model, but it is also relevant that the student is able to interpret the outcomes, and use the simulation as a decision support tool to formulate recommendations on how the process might be improved.
Please note that there is an atypical form of assessment:
50% of the grade is determined by a number of assignments (usually 2). These assignments are usually made in groups of 2 or 3 students. Please note that the number of assignments and number of students per group will not be determined until the start of the course. In fact, it is possible that the teachers decide that one assignment is an individual assignment instead of a group assignment. 50% of the final grade is determined by the final exam, which is an individual programming task. This takes place in an on-campus exam setting, with students programming one or more extensions to the program developed in one of the earlier assignments. The minimum grade for this individual final exam is 4. Students will be given a resit opportunity for this last part.
Although the programming language Python is used during the lectures, the students are free to choose whichever programming language they prefer for their assignments. We also provide sample code in Java and R The course is aimed at experienced programmers, familiar with concepts like object oriented programming
Objectives
The student:- Understands the behaviour of all stochastic processes that are discussed in the course.
- Can develop/identify a suitable mathematical model for practical situations in which randomness plays an important role.
- S/he can develop and implement a simulation for all the discussed stochastic processes, but also for more complicated models encountered in practice. S/he can develop the simulation engine and design the event-handling system.
- Is capable of working in a team, efficiently and in a cooperative manner.
- Can analyse real-life data and use this data (or abstractions thereof) in simulation experiments
- Can apply techniques to analyse (and, if necessary, improve) the accuracy of the simulation outcomes.
- Write a technical report containing (at least) a clear problem description, a description of the model and/or the simulation, the results and conclusions based on the simulation outcomes.
- Is capable of searching appropriate literature on a specific topic to gain more insight or background information. Moreover, s/he can read a scientific paper containing a mathematical model or a practical real-life situation, and build an adequate simulation program for this model