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Lund University

Numerical Analysis: Numerical Methods for Partial Differential Equations

<p>The course is an optional course for second-cycle studies for a Master's degree in mathematics with a specialisation in numerical analysis, as well as for a master's degree in computational science. The purpose of the course is for the students to acquire in-depth knowledge of numerical methods for partial differ…

  • Higher education
  • Information unavailable
  • 19 January 2026
  • Information unavailable
  • Information unavailable
  • 50 %

Overview

<p>The course is an optional course for second-cycle studies for a Master's degree in mathematics with a specialisation in numerical analysis, as well as for a master's degree in computational science. The purpose of the course is for the students to acquire in-depth knowledge of numerical methods for partial differential equations and get the opportunity to work with relevant computational problems and software in connection with partial differential equations.</p><p>The course gives an introduction to error estimates, convergence and stability, as well as existence and regularity of solutions to elliptic differential equations (PDEs). This is followed by an introduction to DUNE-FEM which discusses how elliptic PDEs can be discretized using the Finite Element Method (FEM).</p> <p>The course treats:</p> <ul> <li>Weak theory for elliptic PDEs: existence and error estimates</li> <li>Construction of Finite Elements, e.g. discretization grids, reference elements, degree-of-freedom (DOF) mappings</li> <li>Unified Form Language (UFL) for description of weak forms of partial differential equations</li> <li>Parallelization of Finite Element methods using domain decomposition</li> <li>Adaptive Finite Element</li> </ul><p>https://www.maths.lu.se/english/education/</p>

Admission scores

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Entry requirements

Admission to the course requires English 6/b and at least 90 credits in natural sciences or engineering, of which at least 45 credits should be in mathematics and/or numerical analysis, including knowledge corresponding to the courses NUMA01 Computational Programming with Python, 7.5 credits, and NUMN32 Numerical Methods for Differential Equations, 7.5 credits.

The text is reproduced from the Susa source. Antagningsdata does not map GY11 and GY25 or assess personal eligibility.

Source, measure and data quality
Source
Skolverket Susa-navet
Period
2026-01-19
Measure
Entry-requirement text reproduced from the published Susa data; no personal eligibility assessment is made.
Population
Education offering e.uoh.lu.numn33.52573.20261
Last checked
2026-09-23T10:38:33.477975+00:00
Limitation
Antagningsdata does not map GY11 and GY25. General and specific conditions are not separated without structured source data.

Programme content

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Study structure

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Application and important dates

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  2. Programme or course ends

Salary and salary distribution

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Common occupations after graduation

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Students

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Geographical background

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Previous upper-secondary schools and programmes

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About the provider

Lund University

Provider for the published education offering.

Sources and data quality

Education facts for the selected offering come from Skolverket Susa-navet.

Retrieved . Published . Times are shown in Swedish local time.

Source identity and publication version
Publication version
8e217193-f5fa-4778-b085-a4521fd03e8d
Education identity in the source
i.uoh.lu.numn33.52573.20261
Offering identity in the source
e.uoh.lu.numn33.52573.20261
Education-form source code
HS
Education code in the source
NUMN33
Change time according to the source
2025-07-09T12:38:45

The provider, education and education offering are separate identities. Application information should be checked on the official website. Supplementary statistics have not been obtained from this source.