Suchergebnis: Katalogdaten im Herbstsemester 2018
Rechnergestützte Wissenschaften Bachelor | ||||||
Bachelor-Studium (Studienreglement 2012 und 2016) | ||||||
Grundlagenfächer | ||||||
Block G1 | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
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401-0353-00L | Analysis III | O | 4 KP | 2V + 2U | A. Figalli | |
Kurzbeschreibung | In 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. | |||||
Lernziel | The 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. | |||||
Inhalt | 1.) 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 | |||||
Literatur | Y. Pinchover, J. Rubinstein, "An Introduction to Partial Differential Equations", Cambridge University Press (12. Mai 2005) | |||||
Voraussetzungen / Besonderes | Prerequisites: Analysis I and II, Fourier series (Complex Analysis) | |||||
402-0811-00L | Programming Techniques for Scientific Simulations I | O | 5 KP | 4G | R. Käppeli | |
Kurzbeschreibung | This lecture provides an overview of programming techniques for scientific simulations. The focus is on advances 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. | |||||
Lernziel | ||||||
401-0663-00L | Numerical Methods for CSE | O | 8 KP | 4V + 2U + 1P | R. Alaifari | |
Kurzbeschreibung | The 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++. | |||||
Lernziel | * 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 | |||||
Inhalt | 1. Direct Methods for linear systems of equations 2. Least Squares Techniques 3. Data Interpolation and Fitting 4. Filtering Algorithms 8. Approximation of Functions 9. Numerical Quadrature 10. Iterative Methods for non-linear systems of equations 11. Single Step Methods for ODEs 12. Stiff Integrators | |||||
Skript | Lecture materials (PDF documents and codes) will be made available to the participants through the course web page: Link | |||||
Literatur | U. 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. M. Hanke-Bourgeois "Grundlagen der Numerischen Mathematik und des wissenschaftlichen Rechnens", BG Teubner, 2002 P. Deuflhard and A. Hohmann, "Numerische Mathematik I", DeGruyter, 2002 | |||||
Voraussetzungen / Besonderes | The course will be accompanied by programming exercises in C++ relying on the template library EIGEN. Familiarity with C++, object oriented and generic programming is an advantage. Participants of the course are expected to learn C++ by themselves. | |||||
Block G2 | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
401-0603-00L | Stochastik | O | 4 KP | 2V + 1U | M. H. Maathuis | |
Kurzbeschreibung | Die Vorlesung deckt folgende Themenbereiche ab: Zufallsvariablen, Wahrscheinlichkeit und Wahrscheinlichkeitsverteilungen, gemeinsame und bedingte Wahrscheinlichkeiten und Verteilungen, das Gesetz der Grossen Zahlen, der zentrale Grenzwertsatz, deskriptive Statistik, schliessende Statistik, Statistik bei normalverteilten Daten, Punktschätzungen, und Vergleich zweier Stichproben. | |||||
Lernziel | Kenntnis der Grundlagen der Wahrscheinlichkeitstheorie und Statistik. | |||||
Inhalt | Einführung in die Wahrscheinlichkeitstheorie, einige Grundbegriffe der mathematischen Statistik und Methoden der angewandten Statistik. | |||||
Skript | Vorlesungsskript | |||||
Literatur | Vorlesungsskript | |||||
252-0834-00L | Information Systems for Engineers | O | 4 KP | 2V + 1U | G. Fourny | |
Kurzbeschreibung | This course provides the basics of relational databases from the perspective of the user. We will discover why tables are so incredibly powerful to express relations, learn the SQL query language, and how to make the most of it. The course also covers support for data cubes (analytics). After this course, you will be ready for Big Data for Engineers. | |||||
Lernziel | After visiting this course, you will be capable to: 1. Explain, in the big picture, how a relational database works and what it can do in your own words. 2. Explain the relational data model (tables, rows, attributes, primary keys, foreign keys), formally and informally, including the relational algebra operators (select, project, rename, all kinds of joins, division, cartesian product, union, intersection, etc). 3. Perform non-trivial reading SQL queries on existing relational databases, as well as insert new data, update and delete existing data. 4. Design new schemas to store data in accordance to the real world's constraints, such as relationship cardinality 5. Explain what bad design is and why it matters. 6. Adapt and improve an existing schema to make it more robust against anomalies, thanks to a very good theoretical knowledge of what is called "normal forms". 7. Understand how indices work (hash indices, B-trees), how they are implemented, and how to use them to make queries faster. 8. Access an existing relational database from a host language such as Java, using bridges such as JDBC. 9. Explain what data independence is all about and didn't age a bit since the 1970s. 10. Explain, in the big picture, how a relational database is physically implemented. 11. Know and deal with the natural syntax for relational data, CSV. 12. Explain the data cube model including slicing and dicing. 13. Store data cubes in a relational database. 14. Map cube queries to SQL. 15. Slice and dice cubes in a UI. And of course, you will think that tables are the most wonderful object in the world. | |||||
Inhalt | Using a relational database ================= 1. Introduction 2. The relational model 3. Data definition with SQL 4. The relational algebra 5. Queries with SQL Taking a relational database to the next level ================= 6. Database design theory 7. Databases and host languages 8. Databases and host languages 9. Indices and optimization 10. Database architecture and storage Analytics on top of a relational database ================= 12. Data cubes Outlook ================= 13. Outlook | |||||
Literatur | - Lecture material (slides). - Book: "Database Systems: The Complete Book", H. Garcia-Molina, J.D. Ullman, J. Widom (It is not required to buy the book, as the library has it) | |||||
Voraussetzungen / Besonderes | For non-CS/DS students only, BSc and MSc Elementary knowledge of set theory and logics Knowledge as well as basic experience with a programming language such as Pascal, C, C++, Java, Haskell, Python | |||||
401-0647-00L | Introduction to Mathematical Optimization | O | 5 KP | 2V + 1U | D. Adjiashvili | |
Kurzbeschreibung | Introduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering. | |||||
Lernziel | The 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. | |||||
Inhalt | Topics 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. | |||||
Literatur | Information about relevant literature will be given in the lecture. | |||||
Voraussetzungen / Besonderes | This 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. | |||||
Block G3 Die Lehrveranstaltungen von Block G3 finden im Frühjahrssemester statt. | ||||||
Block G4 Studierende, die aus einem anderen ETH-Studiengang in das zweite Studienjahr des Bachelor-Studiengangs RW übergetreten sind und deren Basisprüfung das Fach "Physik I" nicht umfasst, müssen im Prüfungsblock G4 anstelle von "Physik II" (im Frühjahrssemester) den Jahreskurs "Physik I und II" (402-0043-00L und 402-0044-00L) aus dem Bachelor-Studiengang Chemie belegen und die entsprechende Prüfung ablegen. | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
402-0043-00L | Physik I | W | 4 KP | 3V + 1U | J. Home | |
Kurzbeschreibung | Einführung in die Denk- und Arbeitsweise in der Physik unter Zuhilfenahme von Demonstrationsexperimenten: Mechanik von Massenpunkten und starren Körpern, Schwingungen und Wellen. | |||||
Lernziel | Vermittlung der physikalischen Denk- und Arbeitsweise und Einführung in die Methoden in einer experimentellen Wissenschaft. Die Studenten und Studentinnen soll lernen, physikalische Fragestellungen im eigenen Wissenschaftsbereich zu identifizieren, zu kommunizieren und zu lösen. | |||||
Inhalt | Mechanik (Bewegung, Newtonsche Axiome, Arbeit und Energie, Impulserhaltung, Drehbewegungen, Gravitation, deformierbare Körper) Schwingungen und Wellen (Schwingungen, mechanische Wellen, Akustik) | |||||
Skript | Die Vorlesung richtet sich nach dem Lehrbuch "Physik" von Paul A. Tipler. | |||||
Literatur | Tipler, Paul A., Mosca, Gene, Physik (für Wissenschaftler und Ingenieure), Springer Spektrum | |||||
Kernfächer | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
151-0107-20L | High Performance Computing for Science and Engineering (HPCSE) I | O | 4 KP | 4G | P. Koumoutsakos | |
Kurzbeschreibung | This course gives an introduction into algorithms and numerical methods for parallel computing for multi and many-core architectures and for applications from problems in science and engineering. | |||||
Lernziel | Introduction to HPC for scientists and engineers Fundamental of: 1. Parallel Computing Architectures 2. MultiCores 3. ManyCores | |||||
Inhalt | Parallel Programming models and languages (OpenMP, MPI). Parallel Performance metrics and Code Optimization. Examples based on grid and particle methods for solving Partial Differential Equations and on fundamentals of stochastic optimisation and machine learning. | |||||
Skript | Link Class notes, handouts | |||||
Bachelor-Arbeit Wenn Sie anstelle von 401-2000-00L Scientific Works in Mathematics die Lerneinheit 402-2000-00L Scientific Works in Physics anrechnen lassen möchten (dies ist erlaubt im Studiengang Rechnergestützte Wissenschaften), so wenden Sie sich nach dem Verfügen des Resultates an das Studiensekretariat (Link). | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
401-2000-00L | Scientific Works in Mathematics Zielpublikum: Bachelor-Studierende im dritten Jahr; Master-Studierende, welche noch keine entsprechende Ausbildung vorweisen können. | O | 0 KP | E. Kowalski | ||
Kurzbeschreibung | Introduction to scientific writing for students with focus on publication standards and ethical issues, especially in the case of citations (references to works of others.) | |||||
Lernziel | Learn the basic standards of scientific works in mathematics. | |||||
Inhalt | - Types of mathematical works - Publication standards in pure and applied mathematics - Data handling - Ethical issues - Citation guidelines | |||||
Skript | Moodle of the Mathematics Library: Link | |||||
Voraussetzungen / Besonderes | Weisung Link | |||||
401-2000-01L | Recherchieren in der Mathematik [wird überarbeitet] Für Details und zur Registrierung für den freiwilligen MathBib-Schulungskurs: Link | Z | 0 KP | Referent/innen | ||
Kurzbeschreibung | Freiwilliger Kurs "Recherchieren in der Mathematik" angeboten von der Mathematikbibliothek. | |||||
Lernziel | ||||||
402-2000-00L | Scientific Works in Physics Zielpublikum: Master-Studierende, welche noch keine entsprechende Ausbildung vorweisen können. Weisung Link | W | 0 KP | C. Grab | ||
Kurzbeschreibung | Literature Review: ETH-Library, Journals in Physics, Google Scholar; Thesis Structure: The IMRAD Model; Document Processing: LaTeX and BibTeX, Mathematical Writing, AVETH Survival Guide; ETH Guidelines for Integrity; Authorship Guidelines; ETH Citation Etiquettes; Declaration of Originality. | |||||
Lernziel | Basic standards for scientific works in physics: How to write a Master Thesis. What to know about research integrity. | |||||
401-3990-01L | Bachelor-Arbeit Nur für Rechnergestützte Wissenschaften BSc, Studienreglement 2012 und 2016. Voraussetzung: erfolgreicher Abschluss der Lerneinheit 401-2000-00L Scientific Works in Mathematics oder 402-2000-00L Scientific Works in Physics Weitere Angaben unter Link | O | 8 KP | 11D | Betreuer/innen | |
Kurzbeschreibung | Die Bachelor-Arbeit bildet den Abschluss des Studiengangs. Sie soll einerseits dazu dienen, das Wissen in einem bestimmten Fachgebiet zu vertiefen sowie in einen ersten Kontakt mit Anwendungen zu kommen und Probleme aus solchen Anwendungen in einer bestehenden wissenschaftlichen Gruppe rechnergestützt anzugehen. Die Bachelor-Arbeit umfasst ca. 160 Stunden. | |||||
Lernziel | Die Bachelorarbeit soll einerseits dazu dienen, das Wissen in einem bestimmten Fachgebiet zu vertiefen sowie in einen ersten Kontakt mit Anwendungen zu kommen und Probleme aus solchen Anwendungen rechnergestützt anzugehen. Andererseits soll auch gelernt werden, in einer bestehenden wissenschaftlichen Gruppe mitzuarbeiten. | |||||
Voraussetzungen / Besonderes | Der verantwortliche Leiter der Bachelorarbeit definiert die Aufgabenstellung und legt den Beginn der Bachelorarbeit und den Abgabetermin fest. Die Bachelorarbeit wird mit einem schriftlichen Bericht abgeschlossen. Die Leistung wird mit einer Note bewertet. |
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