401067400L Numerical Methods for Partial Differential Equations
Semester  Spring Semester 2020 
Lecturers  R. Hiptmair 
Periodicity  yearly recurring course 
Language of instruction  English 
Comment  Not meant for BSc/MSc students of mathematics. 
Courses
Number  Title  Hours  Lecturers  

401067400 G  Numerical Methods for Partial Differential Equations This course is designed in a flipped classroom format based on video tutorials and supplemented by a weekly questionandanswer session, for which attendance is highly recommended.  2 hrs 
 R. Hiptmair  
401067400 U  Numerical Methods for Partial Differential Equations Groups are selected in myStudies.  2 hrs 
 R. Hiptmair  
401067400 P  Numerical Methods for Partial Differential Equations Homework C++ coding projects for the course "Numerical Methods for Partial Differential Equations"  2 hrs  R. Hiptmair  
401067400 A  Numerical Methods for Partial Differential Equations Video guided selfstudy or groupstudy for the course "Numerical Methods for Partial Differential Equations"  4 hrs  R. Hiptmair 
Catalogue data
Abstract  Derivation, properties, and implementation of fundamental numerical methods for a few key partial differential equations: convectiondiffusion, heat equation, wave equation, conservation laws. Implementation in C++ based on a 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  1 SecondOrder Scalar Elliptic Boundary Value Problems 1.2 Equilibrium Models: Examples 1.3 Sobolev spaces 1.4 Linear Variational Problems 1.5 Equilibrium Models: Boundary Value Problems 1.6 Diffusion Models (Stationary Heat Conduction) 1.7 Boundary Conditions 1.8 SecondOrder Elliptic Variational Problems 1.9 Essential and Natural Boundary Conditions 2 Finite Element Methods (FEM) 2.2 Principles of Galerkin Discretization 2.3 Case Study: Linear FEM for TwoPoint Boundary Value Problems 2.4 Case Study: Triangular Linear FEM in Two Dimensions 2.5 Building Blocks of General Finite Element Methods 2.6 Lagrangian Finite Element Methods 2.7 Implementation of Finite Element Methods 2.7.1 Mesh Generation and Mesh File Format 2.7.2 Mesh Information and Mesh Data Structures 2.7.2.1 L EHR FEM++ Mesh: Container Layer 2.7.2.2 L EHR FEM++ Mesh: Topology Layer 2.7.2.3 L EHR FEM++ Mesh: Geometry Layer 2.7.3 Vectors and Matrices 2.7.4 Assembly Algorithms 2.7.4.1 Assembly: Localization 2.7.4.2 Assembly: Index Mappings 2.7.4.3 Distribute Assembly Schemes 2.7.4.4 Assembly: Linear Algebra Perspective 2.7.5 Local Computations 2.7.5.1 Analytic Formulas for Entries of Element Matrices 2.7.5.2 Local Quadrature 2.7.6 Treatment of Essential Boundary Conditions 2.8 Parametric Finite Element Methods 3 FEM: Convergence and Accuracy 3.1 Abstract Galerkin Error Estimates 3.2 Empirical (Asymptotic) Convergence of Lagrangian FEM 3.3 A Priori (Asymptotic) Finite Element Error Estimates 3.4 Elliptic Regularity Theory 3.5 Variational Crimes 3.6 FEM: Duality Techniques for Error Estimation 3.7 Discrete Maximum Principle 3.8 Validation and Debugging of Finite Element Codes 4 Beyond FEM: Alternative Discretizations [dropped] 5 NonLinear Elliptic Boundary Value Problems [dropped] 6 SecondOrder Linear Evolution Problems 6.1 TimeDependent Boundary Value Problems 6.2 Parabolic InitialBoundary Value Problems 6.3 Linear Wave Equations 7 ConvectionDiffusion Problems [dropped] 8 Numerical Methods for Conservation Laws 8.1 Conservation Laws: Examples 8.2 Scalar Conservation Laws in 1D 8.3 Conservative Finite Volume (FV) Discretization 8.4 Timestepping for FiniteVolume Methods 8.5 HigherOrder Conservative FiniteVolume Schemes 
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. 
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. 
Prerequisites / Notice  Mastery of basic calculus and linear algebra is taken for granted. Familiarity with fundamental numerical methods (solution methods for linear systems of equations, interpolation, approximation, numerical quadrature, numerical integration of ODEs) is essential. Important: Coding skills and experience in C++ are essential. Homework assignments involve substantial coding, partly based on a C++ finite element library. The written examination will be computer based and will comprise coding tasks. 
Performance assessment
Performance assessment information (valid until the course unit is held again)  
Performance assessment as a semester course  
In examination block for  Bachelor's Degree Programme in Computational Science and Engineering 2016; Version 27.03.2018 (Examination Block G3) Bachelor's Degree Programme in Computational Science and Engineering 2018; Version 24.05.2022 (Examination Block G3) Bachelor's Programme in Computational Science and Engineering 2012; Version 13.12.2016 (Examination Block G3) 
ECTS credits  10 credits 
Examiners  R. Hiptmair 
Type  session examination 
Language of examination  English 
Repetition  The performance assessment is offered every session. Repetition possible without reenrolling for the course unit. 
Admission requirement  None 
Mode of examination  written 180 minutes 
Additional information on mode of examination  Computer based examination involving coding problems beside theoretical questions. Some of the lecture materials will be made available as PDF during the examination. A 30minute midterm exam and a 30minute end term exam will be held during the teaching period on dates specified in the beginning of the semester. The grades of these interim examinations will be taken into account through a bonus of up to 20% for the final grade. 
Written aids  Summary of up to 10 pages A4 in the candidates OWN HANDWRITING. No typeset printouts and copies of other students' notes are allowed. 
Online examination  The examination may take place on the computer. 
If the course unit is part of an examination block, the credits are allocated for the successful completion of the whole block. This information can be updated until the beginning of the semester; information on the examination timetable is binding. 
Learning materials
No public learning materials available.  
Only public learning materials are listed. 
Groups
401067400 U  Numerical Methods for Partial Differential Equations  
Groups  G01 
 
G02 
 
G03 
 
G04 

Restrictions
There are no additional restrictions for the registration. 