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Numerical Methods for Partial Differential Equations
Last Updated: 2026-06-01 11:33:10
Abstract
Derivation, properties, and implementation of fundamental numerical methods for a few key partial differential equations, for instance, convection-diffusion, heat equation, wave equation, conservation laws, Stokes equations, Maxwell equations. Implementation in C++ based on a 2D finite element library.
Objective
Main skills to be acquired in this course: * Ability to implement fundamental numerical methods for the solution of partial differential equations efficiently. * Ability to modify and adapt numerical algorithms guided by awareness of their mathematical foundations. * Ability to select and assess numerical methods in light of the predictions of theory * Ability to identify features of a PDE (= partial differential equation) based model that are relevant for the selection and performance of a numerical algorithm. * Ability to understand research publications on theoretical and practical aspects of numerical methods for partial differential equations. * Skills in the efficient implementation of finite element methods on unstructured meshes. This course is neither a course on the mathematical foundations and numerical analysis of methods nor an course that merely teaches recipes and how to apply software packages.
Content
Second-Order Scalar Elliptic Boundary Value Problems Finite Element Methods (FEM) FEM: Convergence and Accuracy Beyond FEM: Alternative Discretizations Non-Linear Elliptic Boundary Value Problems Second-Order Linear Evolution Problems Finite-Element Exterior Calculus (FEEC) Finite Elements for the Stokes Equation
Resources
Lecture Notes
The lecture will be taught in flipped classroom format:- Video tutorials for all thematic units will be published online.- Tablet notes accompanying the videos will be made available to the audience as PDF.- A comprehensive lecture document will cover all aspects of the course, seehttps://www.sam.math.ethz.ch/~grsam/NUMPDEFL/NUMPDE.pdf
Literature
Chapters of the following books provide supplementary reading (detailed references in course material): * D. Braess: Finite Elemente, Theorie, schnelle Löser und Anwendungen in der Elastizitätstheorie, Springer 2007 (available online). * S. Brenner and R. Scott. Mathematical theory of finite element methods, Springer 2008 (available online). * A. Ern and J.-L. Guermond. Theory and Practice of Finite Elements, volume 159 of Applied Mathematical Sciences. Springer, New York, 2004. * Ch. Großmann and H.-G. Roos: Numerical Treatment of Partial Differential Equations, Springer 2007. * W. Hackbusch. Elliptic Differential Equations. Theory and Numerical Treatment, volume 18 of Springer Series in Computational Mathematics. Springer, Berlin, 1992. * P. Knabner and L. Angermann. Numerical Methods for Elliptic and Parabolic Partial Differential Equations, volume 44 of Texts in Applied Mathematics. Springer, Heidelberg, 2003. * S. Larsson and V. Thomée. Partial Differential Equations with Numerical Methods, volume 45 of Texts in Applied Mathematics. Springer, Heidelberg, 2003. * R. LeVeque. Finite Volume Methods for Hyperbolic Problems. Cambridge Texts in Applied Mathematics. Cambridge University Press, Cambridge, UK, 2002. However, study of supplementary literature is not important for for following the course.
Learning Materials (Links)
- Documents
- NumPDe 2025 Homework Project Collection
- NumPDE 2025 Lecture Document
- Additional links
- NumPDE 2025 Course Repository
General Information
- Language
- English
- Levels
- BSC , MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 225 minutes
- Aids
- None
- Digital
- The exam takes place on devices provided by ETH Zurich.
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Numerical Methods for Partial Differential Equations
This course is designed in a flipped classroom format based on video tutorials and supplemented by a weekly question-and-answer session, for which attendance is highly recommended.
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2 h weekly |
| exercise |
Numerical Methods for Partial Differential Equations
Groups are selected in myStudies.
Tue 14-16 or Thu 14-16 or Fri 8-10
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2 h weekly |
| practical/laboratory course |
Numerical Methods for Partial Differential Equations
Homework C++ coding projects for the course "Numerical Methods for Partial Differential Equations"
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No time listed | 2 h weekly |
| independent project |
Numerical Methods for Partial Differential Equations
Video guided self-study or group-study for the course "Numerical Methods for Partial Differential Equations"
This course coincides with 401-0674-00 Numerical Methods for Partial Differential Equations, which is taught this Spring Term. All students who have to take this course must also enrol in 401-0674-00 Numerical Methods for Partial Differential Equations.
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No time listed | 4 h weekly |
Offered In
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Wahlfächer (Es können auch Lehrveranstaltungen aus dem Master-Studiengang in Informatik gewählt werden. Es liegt in der Verantwortung der Studierenden, sicherzustellen, dass sie die Voraussetzungen für diese Lehrveranstaltungen erfüllen.)
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Computational Biology and Bioinformatics Master (More informations at: )
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Vertiefungsfächer (A total of 30 ECTS needs to be acquired in the Advanced Courses category. Thereof at least 16 ECTS in the Theory and 10 ECTS in the Biology category.)
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Theorie (At least 16 ECTS need to be acquired in this category.)
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Fachspezifische Vertiefung (Es müssen mindestens 20 KP aus den Deep Track Lerneinheiten absolviert werden. Überzählige KP können für Wahlfächer angerechnet werden.)
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Wahlfächer Aerospace Engineering (Diese Fächer können nur als Wahlfach angerechnet werden.)
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