MM547: Ordinary Differential Equations: Theory, Modelling and Simulation
Comment
Entry requirements
Academic preconditions
- 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
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).
- Give the competence to :
- handle complex and development-oriented situations in study and work contexts.
- Give skills to:
- analyse practical and theoretical problems with the help of numerical simulation based on a suitable mathematical model
- analyse the qualitative and quantitative properties of a given problem
- describe and evaluate sources of error for the modelling and calculation of a given problem
- justify relevant models for analysis and solution and choose between them
- Give knowledge and understanding of:
- Mathematical modelling and numerical analysis in science and engineering
- 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:
- Formulate a differential equation as a model for a simple problem
- Solve differential equations by methods taught in the course
- Find steady states and analyse the asymptotic behaviour of simple systems of differential equations.
- Construct, implement and analyse numerical methods to compute (approximate) solutions to differential equations.
- 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
Examination regulations
Exam element a)
Timing
Tests
Mandatory assignments
EKA
Assessment
Grading
Identification
Language
Examination aids
To be announced during the course
ECTS value
Exam element b)
Timing
January
Tests
Oral exam
EKA
Assessment
Grading
Identification
Language
Examination aids
To be announced during the course
ECTS value
Indicative number of lessons
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
Additional teachers
Name | Department | City | |
---|---|---|---|
Alexei Latyntsev | latyntsev@imada.sdu.dk | Institut for Matematik og Datalogi | |
Jens Kaad | kaad@imada.sdu.dk | Institut for Matematik og Datalogi |