MAGIC098: Adaptive Finite Element Methods

Course details

A specialist MAGIC course


Autumn 2023
Monday, October 2nd to Friday, December 8th


Live lecture hours
Recorded lecture hours
Total advised study hours


11:05 - 11:55 (UK)

Course forum

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Adaptive Finite Element Methods is an advanced course in numerical analysis. 
The first aim is to provide an introduction to the finite element method (FEM) itself, which is one of the most powerful methods for approximating solutions to partial differential equations (PDEs). 
The second aim is to provide an introduction to adaptive finite element methods, which are techniques that generate optimal meshes, and thereby provide the most efficient methodology to approximate solutions to PDEs.
Learning objectives:
* Discretise PDEs with FEM
* Analyse the stability and convergence of FEM
* Estimate errors of approximations to PDEs
* Approximate PDEs using adaptive mesh refinement
* Analyse convergence of adaptive methods
* Introduce Neural Network approximations of PDEs
Within this course, there is a mix of methods, theory and algorithms. 


Students are expected to have had an introductory course in numerical analysis. They should also have basic knowledge of partial differential equations (PDEs), and some programming experience.
(It is not assumed students are familiar with the finite element method itself.)


  • Intro FEM I: Weak formulations of PDEs in 1-D
  • Intro FEM II: FEM discretization in 1-D
  • Intro FEM III: Well-posedness of weak formulations of PDEs
  • Intro FEM IV: FEM discretization in 2-D
  • Intro FEM V: Convergence of FEM for PDEs
  • Adaptive FEM I: Computable error estimates for FEM
  • Adaptive FEM II: Goal-oriented error estimates for FEM
  • Adaptive FEM III: Convergence of adaptive FEM for PDEs
  • Adaptive FEM IV: Optimality of adaptive FEM for PDEs
  • Adaptive FEM V: Neural networks and deep learning for PDEs
  • Set of problems similar to examples in Lectures.

Recommended readings supporting lectures:
  • [Larson, Bengzon, 2013] The Finite Element Method: Theory, Implementation, and Applications, Springer Book, Link to preprint 
  • [Johnson, 1987] Numerical Solution of Partial Differential Equations by the Finite Element Method, Cambridge University Press Book, Link
  • [Ern, Guermond, 2021 b] Finite Elements II: Galerkin Approximation Elliptic and Mixed PDEs, Springer Book, Link to preprint
  • [Ainsworth, Oden, 2000] A Posterori Error Estimation in Finite Element Analysis, Wiley Interscience Book, Link
  • [van der Zee, 2004] Goal-Adaptive Discretization of Fluid–Structure Interaction, PhD thesis, Link
  • [Nochetto, 2010] Why Adaptive Finite Element Methods Outperform Classical Ones, Proceedings of the International Congress of Mathematicians, Link
  • [E, Yu, 2018] The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems, Link to preprint
  • [Higham, Higham 2019] Deep Learning: An Introduction for Applied Mathematicians, Link to preprint
Other references used in lectures:
  • [Gander, Wanner, 2012] From Euler, Ritz, and Galerkin to Modern Computing, Link 
  • [Brooks, Hughes, 1982] Streamline upwind/Petrov-Galerkin formulations for convection dominated flows with particular emphasis on the incompressible Navier-Stokes equations, Link
  • [Courant, 1943] Variational methods for the solution of problems of equilibrium and vibrations, Link
  • [Cea, 1964] Approximation variationnelle des problemes aux limites, Link 
  • [Ciarlet, 1978] The Finite Element Method for Elliptic Problems, Link
  • [Babuska, Rheinboldt, 1978] Error Estimates for Adaptive Finite Element Computations, Link 
  • [Reichenbach, 1951] The Rise of Scientific Philosophy, Link
  • [Oden, Prudhomme, 2000] Goal-oriented error estimation and adaptivity for the finite element method, Link
  • [Babuska, Vogelius, 1984] Feedback and adaptive finite element solution of one-dimensional boundary value problems, Link
  • [Dorfler, 1996] A Convergent Adaptive Algorithm for Poisson’s Equation, Link
  • [Binev, Dahmen, DeVore, 2004] Adaptive Finite Element Methods with convergence rates, Link
  • [Strang, 2018] The Functions of Deep Learning, Link


  • Professor Kristoffer van der Zee

    Professor Kristoffer van der Zee

    University of Nottingham


No bibliography has been specified for this course.


The assessment for this course will be released on Monday 8th January 2024 at 00:00 and is due in before Friday 19th January 2024 at 11:00.

Assessment for all MAGIC courses is via take-home exam which will be made available at the release date (the start of the exam period).

You will need to upload a PDF file with your own attempted solutions by the due date (the end of the exam period).

If you have kept up-to-date with the course, the expectation is it should take at most 3 hours’ work to attain the pass mark, which is 50%.

Please note that you are not registered for assessment on this course.


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