Numerical Modeling 1 (MAF310)

Content

Many mathematical problems in science and engineering do not permit simple analytic solutions, necessitating the use of numerical methods. This course provides theoretical insights into fundamental numerical algorithms for problem-solving, along with their practical implementation using the Python programming language. Topics covered include systems of linear equations, interpolation and curve fitting, solutions to nonlinear equations, numerical differentiation and integration, ordinary differential equations, and partial differential equations.

The course runs parallel with MAF320 Numerical Modeling 2, which focuses more on the programming and practical problem-solving aspects of numerical modeling.

Learning outcome

After completing this course, students are expected to:

K1: Have knowledge of key numerical methods, including strategies for constructing and analyzing these methods.

K2: Understand the limitations of numerical methods, such as discretization errors and various instabilities.

F1: Be able to use the Python programming language to solve basic numerical problems using pre-existing algorithms in the NumPy and SciPy packages, with confidence and an understanding of numerical uncertainties.

G1: Master the presentation and communication of computational results using the interactive computing platform Jupyter Notebook.

Required prerequisite knowledge

Recommended prerequisites

Exam

Coursework requirements

Two mandatory assignments; approved/not approved.

Both must be passed in order to gain admission to the written final exam

Course teacher(s)

Course coordinator:

Head of Department:

Method of work

Open for

Bachelor level at Faculty og Science and Technology

Master level at Faculty of Science and Technology.

Course assessment

Literature