# Search result: Catalogue data in Autumn Semester 2021

Computational Science and Engineering Bachelor
Basic Courses
Block G1
NumberTitleTypeECTSHoursLecturers
401-0353-00LAnalysis 3 O4 credits2V + 2UM. Iacobelli
AbstractIn this lecture we treat problems in applied analysis. The focus lies on the solution of quasilinear first order PDEs with the method of characteristics, and on the study of three fundamental types of partial differential equations of second order: the Laplace equation, the heat equation, and the wave equation.
ObjectiveThe aim of this class is to provide students with a general overview of first and second order PDEs, and teach them how to solve some of these equations using characteristics and/or separation of variables.
Content1.) General introduction to PDEs and their classification (linear, quasilinear, semilinear, nonlinear / elliptic, parabolic, hyperbolic)

2.) Quasilinear first order PDEs
- Solution with the method of characteristics
- COnservation laws

3.) Hyperbolic PDEs
- wave equation
- d'Alembert formula in (1+1)-dimensions
- method of separation of variables

4.) Parabolic PDEs
- heat equation
- maximum principle
- method of separation of variables

5.) Elliptic PDEs
- Laplace equation
- maximum principle
- method of separation of variables
- variational method
LiteratureY. Pinchover, J. Rubinstein, "An Introduction to Partial Differential Equations", Cambridge University Press (12. Mai 2005)
Prerequisites / NoticePrerequisites: Analysis I and II, Fourier series (Complex Analysis)
401-0647-00LIntroduction to Mathematical OptimizationO5 credits2V + 1UD. Adjiashvili
AbstractIntroduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering.
ObjectiveThe goal of the course is to obtain a good understanding of some of the most fundamental mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems. The students will also practice applying the learned models to problems in engineering.
ContentTopics covered in this course include:
- Linear programming (simplex method, duality theory, shadow prices, ...).
- Basic combinatorial optimization problems (spanning trees, shortest paths, network flows, ...).
- Modelling with mathematical optimization: applications of mathematical programming in engineering.
LiteratureInformation about relevant literature will be given in the lecture.
Prerequisites / NoticeThis course is meant for students who did not already attend the course "Mathematical Optimization", which is a more advance lecture covering similar topics. Compared to "Mathematical Optimization", this course has a stronger focus on modeling and applications.
401-2673-00LNumerical Methods for CSEO9 credits2V + 2U + 4PR. Hiptmair
AbstractThe course gives an introduction into fundamental techniques and algorithms of numerical mathematics which play a central role in numerical simulations in science and technology. The course focuses on fundamental ideas and algorithmic aspects of numerical methods. The exercises involve actual implementation of numerical methods in C++.
Objective* Knowledge of the fundamental algorithms in numerical mathematics
* Knowledge of the essential terms in numerical mathematics and the
techniques used for the analysis of numerical algorithms
* Ability to choose the appropriate numerical method for concrete problems
* Ability to interpret numerical results
* Ability to implement numerical algorithms afficiently
Content* Computing with Matrices and Vectors
* Direct Methods for linear systems of equations
* Least Squares Techniques
* Data Interpolation and Fitting
* Iterative Methods for non-linear systems of equations
* Filtering Algorithms
* Approximation of Functions
Lecture notesLecture materials (PDF documents and codes) will be made available to the participants through the course web page, whose address will be announced in the beginning of the course.
LiteratureU. ASCHER AND C. GREIF, A First Course in Numerical Methods, SIAM, Philadelphia, 2011.

A. QUARTERONI, R. SACCO, AND F. SALERI, Numerical mathematics, vol. 37 of Texts in Applied Mathematics, Springer, New York, 2000.

W. Dahmen, A. Reusken "Numerik für Ingenieure und Naturwissenschaftler", Springer 2006.

W. Gander, M.J. Gander, and F. Kwok "Scientific Computing", Springer 2014.

M. Hanke-Bourgeois "Grundlagen der Numerischen Mathematik und des wissenschaftlichen Rechnens", BG Teubner, 2002

P. Deuflhard and A. Hohmann, "Numerische Mathematik I", DeGruyter, 2002
Prerequisites / NoticeThe course will be accompanied by programming exercises in C++ relying on the template library EIGEN. Knowledge of C++ is taken for granted.
Competencies
 Subject-specific Competencies Concepts and Theories assessed Techniques and Technologies assessed Method-specific Competencies Analytical Competencies assessed Media and Digital Technologies fostered Problem-solving assessed Project Management fostered
Block G2
NumberTitleTypeECTSHoursLecturers
402-0811-00LProgramming Techniques for Scientific Simulations IO5 credits4GR. Käppeli
AbstractThis lecture provides an overview of programming techniques for scientific simulations. The focus is on basic and advanced C++ programming techniques and scientific software libraries. Based on an overview over the hardware components of PCs and supercomputer, optimization methods for scientific simulation codes are explained.
ObjectiveThe goal of the course is that students learn basic and advanced programming techniques and scientific software libraries as used and applied for scientific simulations.
252-0061-00LSystems Programming and Computer Architecture O7 credits4V + 2UT. Roscoe, A. Klimovic
AbstractIntroduction to systems programming. C and assembly language,
floating point arithmetic, basic translation of C into assembler,
compiler optimizations, manual optimizations. How hardware features
like superscalar architecture, exceptions and interrupts, caches,
virtual memory, multicore processors, devices, and memory systems
function and affect correctness, performance, and optimization.
ObjectiveThe course objectives are for students to:

1. Develop a deep understanding of, and intuition about, the execution
of all the layers (compiler, runtime, OS, etc.) between programs in
high-level languages and the underlying hardware: the impact of
compiler decisions, the role of the operating system, the effects
of hardware on code performance and scalability, etc.

2. Be able to write correct, efficient programs on modern hardware,
not only in C but high-level languages as well.

3. Understand Systems Programming as a complement to other disciplines
within Computer Science and other forms of software development.

This course does not cover how to design or build a processor or
computer.
ContentThis course provides an overview of "computers" as a
platform for the execution of (compiled) computer programs. This
course provides a programmer's view of how computer systems execute
programs, store information, and communicate. The course introduces
the major computer architecture structures that have direct influence
on the execution of programs (processors with registers, caches, other
levels of the memory hierarchy, supervisor/kernel mode, and I/O
structures) and covers implementation and representation issues only
to the extend that they are necessary to understand the structure and
operation of a computer system.

The course attempts to expose students to the practical issues that
affect performance, portability, security, robustness, and
extensibility. This course provides a foundation for subsequent
courses on operating systems, networks, compilers and many other
courses that require an understanding of the system-level
issues. Topics covered include: machine-level code and its generation
by optimizing compilers, address translation, input and output,
trap/event handlers, performance evaluation and optimization (with a
focus on the practical aspects of data collection and analysis).
Lecture notes- C programmnig
- Integers
- Pointers and dynamic memory allocation
- Basic computer architecture
- Compiling C control flow and data structures
- Code vulnerabilities
- Implementing memory allocation
- Floating point
- Optimizing compilers
- Architecture and optimization
- Caches
- Exceptions
- Virtual memory
- Multicore
- Devices
LiteratureThe course is based in part on "Computer Systems: A Programmer's Perspective" (3rd Edition) by R. Bryant and D. O'Hallaron, with additional material.
Prerequisites / Notice252-0029-00L Parallel Programming
252-0028-00L Design of Digital Circuits
Block G3
All course units within Block G3 are offered in the spring semester.
Block G4
All course units within Block G4 are offered in the spring semester.
•  Page  1  of  1