Program Overview

Course Overview

The course DD2363, Methods in Scientific Computing, is a 7.5-credit course that presents numerical methods and algorithms for fundamental models in computational science. The course focuses on particle models, ordinary differential equations, and partial differential equations, with an emphasis on research topics such as machine learning, parallel, and distributed computing.

Information per Course Offering

Spring 2026

• Course location: KTH Campus
• Duration: 13 January 2026 - 13 March 2026
• Periods: Spring 2026: P3 (7.5 hp)
• Pace of study: 50%
• Application code: 60238
• Form of study: Normal Daytime
• Language of instruction: English
• Course memo: Not published
• Number of places: Not limited
• Target group: Open to all students from year 3 and for students admitted to a master's programme as long as it can be included in their programme.
• Planned modular schedule: P3: E1, E2, G2.
• Schedule: Available through a link.
• Part of programme: Master's Programme, Computer Science, year 1, CSSC

Course Staff

• Examiner: Johan Hoffman
• Course coordinator: Johan Hoffman
• Teachers: Johan Hoffman

Course Syllabus

The course syllabus is available in an accessible format on this page. Headings with content from the Course syllabus DD2363 (Spring 2019–) are denoted with an asterisk.

Content and Learning Outcomes

Course Contents

The course focuses on three fields:

• Particle models: Explicit time-step methods, N-body problem, and sparse approximations. Applications include the solar system, mass-spring systems, or molecular dynamics.
• ODE models: Implicit time-step methods, algorithms for sparse systems of non-linear equations. Applications include population dynamics, system biology, or chemical reactions.
• PDE models: Space discretisation through particles, structured grids, or unstructured grids. Grid algorithms, refinement, coarsening, optimisation. Stencil methods, function approximation, Galerkin's method, the finite element method. For each area, computer implementation and algorithms for parallel and distributed computation are discussed and practiced in computer exercises.

Intended Learning Outcomes

After passing the course, the student should be able to:

• Design and implement explicit time-step methods for particle models.
• Design and implement implicit time-step methods for general systems of ordinary differential equations (ODE).
• Design and implement algorithms for systems of non-linear equations.
• Formulate finite element methods (FEM) for partial differential equations (PDE) and adapt FEM software to a given problem.
• Suggest appropriate parallelisation strategy for a given particle model, ODE, or PDE.

Literature and Preparations

Specific Prerequisites

90 credits, of which 45 credits should be in mathematics and/or informatics.

Literature

Will be announced four weeks before the start of the course.

Examination and Completion

Grading Scale

A, B, C, D, E, FX, F

Examination

• TEN1 - Examination, 4.5 credits, grading scale: A, B, C, D, E, FX, F
• LAB1 - Laboratory Assignments, 3.0 credits, grading scale: P, F Based on recommendation from KTH's coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability. The examiner may apply another examination format when re-examining individual students. If the course is discontinued, students may request to be examined during the following two academic years.

Further Information

Course Room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas.

Offered by

EECS/Computer Science

Main Field of Study

Computer Science and Engineering

Education Cycle

Second cycle

Supplementary Information

In this course, the EECS code of honor applies.