DD2363 Methods in Scientific Computing 7.5 credits

Vetenskapliga beräkningsmetoder

The aim of the course is to present general and effective numerical methods and algorithms for basic models in scientific computing, especially particle models, ordinary differential equations (ODE) and partial differential equations (PDE). Challenges in the research field are emphasised, e.g. regarding parallel and distributed calculations.

Show course information based on the chosen semester and course offering:

Offering and execution

No offering selected

Select the semester and course offering above to get information from the correct course syllabus and course offering.

Course information

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.

Course Disposition

No information inserted

Literature and preparations

Specific prerequisites *

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

Recommended prerequisites

No information inserted

Equipment

No information inserted

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 *

  • 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

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Johan Hoffman

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web DD2363

Offered by

EECS/Computer Science

Main field of study *

Computer Science and Engineering

Education cycle *

Second cycle

Add-on studies

No information inserted

Contact

Johan Hoffman, e-post: jhoffman@kth.se

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.

Supplementary information

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex