MM547: Ordinary Differential Equations: Theory, Modelling and Simulation

Study Board of Science

Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N300009112, N300009102
Assessment: Second examiner: None, Second examiner: Internal
Grading: Pass/Fail, 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Bachelor

STADS ID (UVA): N300009101
ECTS value: 10

Date of Approval: 25-04-2019


Duration: 1 semester

Version: Approved - active

Comment

The course is co-read with MM531, MM831 og MM507.

Entry requirements

The course cannot be chosen by students, who followed MM507, MM531, MM545, or MM831.

Academic preconditions

taking the course are expected to:

  • Know the concept of a
    function, real and complex numbers, differentiation and integration of
    functions of one and several variables, vector calculus, convergence of
    sequences, Banach’s fixed point theorem, Newton’s method.
  • Be
    familiar with: systems of linear equations, matrices, determinants, 
    vector spaces, scalar product and orthogonality, linear transformations,
    eigenvectors and eigenvalues, diagonalization, polynomials, random
    variables, normal distribution
  • Have knowledge of how to
    implement algorithms as computer programs and compute numerical
    approximations to mathematical problems that don't allow a closed form
    solution.

Course introduction

The purpose of the course is to introduce modelling of problems from
science and engineering by ordinary differential equations and to
analyse and solve these equations both by analytic tools (when
appropriate) and by computational methods.

The course builds on the
knowledge acquired in the courses MM536 (Calculus for Mathematics),
MM533 (Mathematical and Numerical Analysis), and one of MM505 (Linear
Algebra) or MM538 (Algebra and Linear Algebra). The course is of high
multidisciplinary value and gives an academic basis for a Bachelor
Project in several core areas of Natural Sciences, as well as the
courses MM546 (Partial differential equations: theory, modelling and
simulation) and MM5CC (Computational physics).

In relation to the competence profile of the degree it is the explicit focus of the course to:

  • Give the competence to :
    1. handle complex and development-oriented situations in study and work contexts.
  • Give skills to:
    1. analyse practical and theoretical problems with the help of numerical simulation based on a suitable mathematical model
    2. analyse the qualitative and quantitative properties of a given problem
    3. describe and evaluate sources of error for the modelling and calculation of a given problem
    4. justify relevant models for analysis and solution and choose between them
  • Give knowledge and understanding of:
    1. Mathematical modelling and numerical analysis in science and engineering
    2. reflection on theories, methods and practices in the field of applied mathematics.

Expected learning outcome

The learning objectives of the course is that the student demonstrates the ability to:

  1. Formulate a differential equation as a model for a simple problem
  2. Solve differential equations by methods taught in the course
  3. Find steady states and analyse the asymptotic behaviour of simple systems of differential equations.
  4. Construct, implement and analyse numerical methods to compute (approximate) solutions to differential equations.
  5. Give
    an oral presentation and answer supplementary questions on the course
    syllabus and the problems solved in mandatory assignments.

Content

The following main topics are contained in the course:

1.1. First order differential equations and mathematical models.
1.2. Slope fields and initial value problems.
1.3. Euler's approximation.
1.4. Existence and uniqueness, Picard-Lindelöf theorem (as application of fixed point theorem).
1.5. Gronwall's Lemma and the convergence of Euler's method.
1.6. Analytic tools: integrating factors, separation of variables, and exact equations.
2.1.
Systems of first order linear differential equations, and linear higher
order differential equations: fundamental solutions, the solution
space.
2.2. The Wronskian, Abel's theorem.
2.3. Analytic tools: undetermined coefficients and the variation of parameters.
3. Numerical methods: (embedded) Runge-Kutta methods and adaptivity.
4. Stiffness, implicit methods, A-stability.
5.1. Introduction to Ito-SDEs: Ito integral, Ito process, Ito formula.
5.2 Numerical methods for SDEs: Euler-Maruyama and Milstein methods, weak and strong convergence.

Literature

See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

Autumn

Tests

Mandatory assignments

EKA

N300009112

Assessment

Second examiner: None

Grading

Pass/Fail

Identification

Full name and SDU username

Language

Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value

5

Exam element b)

Timing

January

Tests

Oral exam

EKA

N300009102

Assessment

Second examiner: Internal

Grading

7-point grading scale

Identification

Student Identification Card

Language

Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value

5

Indicative number of lessons

84 hours per semester

Teaching Method

In order to enable students to achieve the learning objectives for the course, the teaching is organised in such a way that there are 84 lectures, class lessons, etc. on a semester. These teaching activities are reflected in an estimated allocation of the workload of an average student as follows:

  • Intro phase (lectures, class lessons) - 56 hours
  • Training phase: 28 hours

Activities during the study phase:

  • preparation of exercises in study groups
  • preparation of projects
  • contributing to online learning activities related to the course

Teacher responsible

Name E-mail Department
Ralf Zimmermann zimmermann@imada.sdu.dk Computational Science

Additional teachers

Name E-mail Department City
Alexei Latyntsev latyntsev@imada.sdu.dk Institut for Matematik og Datalogi
Jens Kaad kaad@imada.sdu.dk Institut for Matematik og Datalogi

Timetable

Administrative Unit

Institut for Matematik og Datalogi (matematik)

Team at Educational Law & Registration

NAT

Offered in

Odense

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