Teaching plan for the course unit

 

 
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General information

 

Course unit name: Mathematical Models and Dynamical Systems

Course unit code: 360153

Academic year: 2022-2023

Coordinator: Xavier Jarque Ribera

Department: Department of Mathematics and Computer Science

Credits: 6

Single program: S

 

 

Estimated learning time

Total number of hours 150

 

Face-to-face and/or online activities

60
 

-  Lecture

Face-to-face

  

30
 

-  Problem-solving class

Face-to-face

  

15
 

-  Laboratory session

Face-to-face

  

15

Supervised project

30

Independent learning

60

 

 

Competences to be gained during study

 

   -

Ability to use physical and electronic bibliographical resources.
   -

Basic knowledge and understanding of mathematics.
   -

Understanding of and capacity to apply the language of mathematics.
   -

Ability to identify flaws in scientific reasoning.
   -

Capacity to select and apply the most suitable mathematical process for each problem encountered.
   -

Capacity to construct mathematical models of simple, real-life situations.
   -

Capacity to assimilate new mathematical concepts through the prior understanding of other concepts.
   -

Understanding of the applications of mathematics in other branches of science and technology.

Learning objectives

 

Referring to knowledge

— Understand and interpret various mathematical models from different scientific fields (biology, physics, economics, etc.).

 

— Understand some of the key results of dynamic systems theory.

 

Referring to abilities, skills

— Solve difference equations and elementary differential equations.

 

— Qualitatively analyse the dynamics of low-dimensional discrete and continuous systems.

 

— Apply dynamic systems tools in the analysis of mathematical models.

 

 

Teaching blocks

 

1. Discrete dynamical systems

1.1. Discrete evolutionary processes: application iteration

1.2. Linear difference equations

1.3. One-dimensional applications: qualitative analysis, phase portraits and bifurcation

1.4. Discrete linear systems: resolution, qualitative analysis and classification

1.5. Introduction to qualitative theory: stationary solution and stability

1.6. Applications

2. Fractals and complex dynamics

2.1. Iterated function systems

2.2. Fractal dimension

2.3. Fractals and dynamic systems

2.4. Julia and Mandelbrot sets

3. Continuous dynamic systems

3.1. Continuous evolution process: ordinary differential equations

3.2. Standard methods of solving ordinary differential equations

3.3. One-dimensional systems: qualitative analysis and phase portraits

3.4. Linear differential equation: resolution, qualitative analysis and classification

3.5. Introduction to qualitative theory: stationary solution and stability

3.6. Applications

 

 

Teaching methods and general organization

 

The weekly timetable consists of three hours of theory classes with a practical component and one hour of laboratory exercises and practical work. The theory classes with a practical component are devoted to presenting the subject matter, which will be illustrated through examples and practical applications. These sessions will be held in face-to-face and/or online mode.

Laboratory classes are dedicated to problem-solving exercises, and the occasional use of computer tools to further understanding of the concepts introduced in the theory classes. Laboratory classes will be held face-to-face, health situation permitting.� Otherwise, practical material will be posted on the Virtual Campus and sessions via BB Collaborate or similar will be held to address any concerns.

Face-to-face sessions may be devoted to carry out assessment activities.

 

 

Official assessment of learning outcomes

 

Continuous assessment is based on a mid-semester examination (P1) and a final examination, of two parts, at the end of the semester, from which two scores are generated (F1 and F2) corresponding to each of the parts, respectively. There is the possibility of sitting the first part of the final examination to increase the grade. A minimum grade of 3 out of 10 is required in each section in order to pass the subject. Also, part of the grade corresponds to an evaluation of the problem-solving work done in the laboratory (L) during the course. The teacher will specify the value of this component at the beginning of the course.

The final grade is obtained by the formula NF = 0.8 * NE + 0.2 * L, where NE = 0.5 * (max [P1, F1] + F2).
Students who do not obtain a score for each of the two parts of the course will be marked as absent.

Once the grading period has passed, there is the possibility of repeat assessment (R), which consists of a single examination covering the entire subject. A minimum final grade of 2.5 is required to be eligible to repeat assessment. Students who have a final grade of “Absent” are not eligible for repeat assessment. To be entered for repeat assessment, students must resign their previous final grade. The final grade for the subject is then the mark obtained in R.

Depending on the health situation, the examinations will be held in face-to-face or online mode. If the assessment must be carried out virtually, this may be supplemented with an interview.

 

Examination-based assessment

Single assessment consists of a final examination (F). This option must be requested of the Secretary’s Office at the Faculty before the date set by the Academic Council.

There is the possibility of repeat assessment (R). A minimum final grade of 2.5 is required to be eligible to repeat assessment. To be entered for repeat assessment, students must resign their previous final grade. The final grade for the subject is then the mark obtained in R.

Depending on the health situation, the examinations will be held in face-to-face or online mode. If the assessment must be carried out virtually, this may be supplemented with an interview.