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DD2363 Methods in Scientific Computing 7.5 credits

The course presents numerical methods and algorithms for fundamental models in computational science, specifically particle models, ordinary differential equations and partial differential equations. Research topics are emphasised, e.g. regarding machine learning, parallel and distributed computing.

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For course offering

Spring 2025 metsc25 programme students

Application code


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 e g on the solar system, mass-spring systems or molecular dynamics.

• ODE models. Implicit time-step methods, algorithms for sparse systems of non-linear equations. Applications in e g 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, which also is 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 (FIVE) 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.

Recommended prerequisites

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

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

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


  • LAB1 - Laboratory Assignments, 3.0 credits, grading scale: P, F
  • TEN1 - Examination, 4.5 credits, grading scale: A, B, C, D, E, FX, 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.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

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Johan Hoffman, e-post:

Supplementary information

In this course, the EECS code of honor applies, see: