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The main prerequisite is a strong motivation to undertake research related in modern aspects functional approximation theory, data compression, related algorithms, or the numerical analysis of partial differential equations.
A solid background in undergraduate analysis and partial differential equations, some basic functional or harmonic analysis, or numerical analysis will be useful.


Autumn 2019 (Monday, October 7 to Friday, December 13)


  • Live lecture hours: 10
  • Recorded lecture hours: 0
  • Total advised study hours: 40


  • Mon 10:05 - 10:55


Requirements are standard year 3 or master's level Analysis and some knowledge of elliptic partial differential equations.
Exposure to Galerkin or finite element methods (as taught in spring term MAGIC-100 or equivalent) will be helpful though not essential. "Review" material will be uploaded.


We start by reviewing the standard Galerkin method with a focus on numerical approximation methods such as wavelet Galekrin, finite elements and discontinuous Galerkin for elliptic and parabolic equations, including the needed element of functional analysis, e.g., Sobolev and Besov spaces. We then recall the apriori error analysis of such methods and move onto aposteriori error analysis. We follow up this with an overview of the literature on adaptive methods and their convergence analysis with a focus on complexity of algorithms. Time allowing we look at connections between wavelet and Galerkin methods or space-time methods for parabolic (perhaps hyperbolic) problems. (NB to be reduced to 10 hours)

Other courses that you may be interested in:


Omar Lakkis (main contact)
Phone 01273 876812
Photo of Omar Lakkis
Chandrasekhar Venkataraman
Phone 01273 876617


MAGIC-098-AFEM 2019 boardshotsOmar Lakkis and Chandrasekhar Venkataraman
Finite elements: theory, fast solvers, and applications in elasticity theoryDietrich Braess
A posteriori error estimation techniques for finite element methodsRüdiger Verfürth
iFEM and adaptive finite element computational package in Matlab (R)Long Chen
Convergence of adaptive finite element methodsMorin, P., R. Nochetto and K. Siebert
Adaptive finite element methods with convergence ratesBinev, Peter, Wolfgang Dahmen and Ron DeVore
Approximation and learning by greedy algorithmsBarron, Andrew R. et al.
Quasi-optimal convergence rate for an adaptive fi- nite element methodCascon, J. Manuel et al.
Axioms of AdaptivityCarstensen, Carsten et al.


Clicking on the link for a book will take you to the relevant Google Book Search page. You may be able to preview the book there. On the right hand side you will see links to places where you can buy the book. There is also link marked 'Find this book in a library'. This sometimes works well, but not always. (You will need to enter your location, but it will be saved after you do that for the first time.)


The assessment consists of one long question addressing a research aspect of Adaptive Finite Element Methods, including aposteriori error estimation, adaptive algorithms or approximation theory.
The question is divided into 8 to 10 subtasks with a mixture of analytical and computational issues to be addressed by the student.
The appropriate bibliography to be used for the assessment will be provided, for you to focus on the contents rather than searching for sources.
The assessment aims at gauging the overall understanding rather than the technical specifics with a pass/fail classification. 50


Files:Exam paper
Released: Monday 6 January 2020 (184.6 days ago)
Deadline: Sunday 19 January 2020 (170.6 days ago)

See instructions on the file itself. In case of any date/deadline conflict the website information overrides anything written (by me) on file.


Files marked L are intended to be displayed on the main screen during lectures.


Recorded Lectures

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