# Roger Käppeli: Catalogue data in Autumn Semester 2021

 Name Dr. Roger Käppeli Address Professur für Angew. MathematikETH Zürich, HG G 52.1Rämistrasse 1018092 ZürichSWITZERLAND Telephone +41 44 632 84 95 E-mail roger.kaeppeli@sam.math.ethz.ch URL https://people.math.ethz.ch/~karoger/ Department Mathematics Relationship Lecturer

NumberTitleECTSHoursLecturers
401-0435-00LComputational Methods for Engineering Applications 4 credits2V + 2UR. Käppeli, M. Petrella
AbstractThe course gives an introduction to the numerical methods for the solution of ordinary and partial differential equations that play a central role in engineering applications. Both basic theoretical concepts and implementation techniques necessary to understand and master the methods will be addressed.
ObjectiveAt the end of the course the students should be able to:

- implement numerical methods for the solution of ODEs (= ordinary differential equations);
- identify features of a PDE (= partial differential equation) based model that are relevant for the selection and performance of a numerical algorithm;
- implement the finite difference, finite element and finite volume method for the solution of simple PDEs using C++;
- read engineering research papers on numerical methods for ODEs or PDEs.
ContentInitial value problems for ODE: review of basic theory for ODEs, Forward and Backward Euler methods, Taylor series methods, Runge-Kutta methods, basic stability and consistency analysis, numerical solution of stiff ODEs.

Two-point boundary value problems: Green's function representation of solutions, Maximum principle, finite difference schemes, stability analysis.

Elliptic equations: Laplace's equation in one and two space dimensions, finite element methods, implementation of finite elements, error analysis.

Parabolic equations: Heat equation, Fourier series representation, maximum principles, Finite difference schemes, Forward (backward) Euler, Crank-Nicolson method, stability analysis.

Hyperbolic equations: Linear advection equation, method of characteristics, upwind schemes and their stability.
Lecture notesScript will be provided.
LiteratureChapters of the following book provide supplementary reading and are not meant as course material:

- A. Tveito and R. Winther, Introduction to Partial Differential Equations. A Computational Approach, Springer, 2005.
Prerequisites / Notice(Suggested) Prerequisites:
Analysis I-III (for D-MAVT), Linear Algebra, Models, Algorithms and Data: Introduction to Computing, basic familiarity with programming in C++.
401-0675-00LStatistical and Numerical Methods for Chemical Engineers3 credits2V + 2UR. Käppeli, P. Müller, A. Ruf, C.‑J. Shih, M. Sokolov
AbstractThis course covers common numerical algorithms and statistical methods used by chemical engineers to solve typical problems arising in industrial and research practice.
ObjectiveThis course covers common numerical algorithms and statistical methods used by chemical engineers to solve typical problems arising in industrial and research practice. The focus is on application of these algorithms to real world problems, while the underlying mathematical principles are also explained. The MATLAB environment is adopted to integrate computation, visualization and programming.
ContentTopics covered:

Part I: Numerical Methods:
- Interpolation & Numerical Calculus
- Non-linear Equations
- Ordinary Differential Equations
- Partial Differential Equations
- Linear and Non-linear Least Squares

Part II: Statistical Methods:
- Data analysis and regression methods
- Statistical experimental design
- Multivariate analysis of spectra
Lecture notesFor the numerics part, see http://www.sam.math.ethz.ch/~karoger/numci/2020/

For the statistics part, see http://stat.ethz.ch/lectures/as21/statistical-numerical-methods.php