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Numerical Methods for Partial Differential Equations
Last Updated: 2026-06-03 00:14:12
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 Second-Order Linear Evolution Problems Convection-Diffus Finite Elements for the Stokes Equation Finite-Element Exterior Calculus (FEEC)
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. * Douglas Arnold, Finite Element Exterior Calculus, SIAM, 2018. However, study of supplementary literature is not important for for following the course.
Learning Materials (Links)
- Documents
- NumPDE 2026 Homework Project Collection
- NumPDE 2026 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
Groups are selected in myStudies.
Q&A session takes place Friday afternoon.
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.
Exercise classes: Tue 16-18 or Thu 14-16
|
|
4 h weekly |
| exercise |
Numerical Methods for Partial Differential Equations
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"
|
No time listed | 2 h weekly |
| revision course / private study |
Numerical Methods for Partial Differential Equations
Video guided self-study or group-study and homework C++ coding projects for the course "Numerical Methods for Partial Differential Equations"
|
No time listed | 6 h weekly |
Offered In
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Electives (Students may also choose courses from the Master's program in Computer Science. It is their responsibility to make sure that they meet the requirements and conditions for these courses.)
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Computational Biology and Bioinformatics Master (More informations at: )
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Advanced Courses (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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Theory (At least 16 ECTS need to be acquired in this category.)
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Deep Track Courses (At least 20 credits must be completed within the deep track courses. Surplus credit points can be counted towards the electives.)
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Elective Courses Aerospace Engineering (These subjects can only be credited as electives.)
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