Search result: Catalogue data in Spring Semester 2012
|Computational Science and Engineering Master|
|151-0110-00L||Compressible Flows||W||4 credits||2V + 1U||J.‑P. Kunsch|
|Abstract||Topics: unsteady one-dimensional subsonic and supersonic flows, acoustics, sound propagation, supersonic flows with shocks and Prandtl-Meyer expansions, flow around slender bodies, shock tubes, reaction fronts (deflagration and detonation). |
Mathematical tools: method of characteristics and selected numerical methods.
|Objective||Illustration of compressible flow phenomena and introduction to the corresponding mathematical description methods.|
|Content||The interaction of compressibility and inertia is responsible for wave generation in a fluid. The compressibility plays an important role for example in unsteady phenomena, such as oscillations in gas pipelines or exhaust pipes. Compressibility effects are also important in steady subsonic flows with high Mach numbers (M>0.3) and in supersonic flows (e.g. aeronautics, turbomachinery).|
The first part of the lecture deals with wave propagation phenomena in one-dimensional subsonic and supersonic flows. The discussion includes waves with small amplitudes in an acoustic approximation and waves with large amplitudes with possible shock formation.
The second part deals with plane, steady supersonic flows. Slender bodies in a parallel flow are considered as small perturbations of the flow and can be treated by means of acoustic methods. The description of the two-dimensional supersonic flow around bodies with arbitrary shapes includes oblique shocks and Prandtl-Meyer expansions etc.. Various boundary conditions, which are imposed for example by walls or free-jet boundaries, and interactions, reflections etc. are taken into account.
|Lecture notes||not available|
|Literature||a list of recommended textbooks is handed out at the beginning of the lecture.|
|Prerequisites / Notice||prerequisites: Fluiddynamics I and II|
|151-0834-00L||Forming Technology II - Introduction Virtual Process Modelling||W||4 credits||2V + 2U||P. Hora|
|Abstract||The lecture imparts the principles of the nonlinear Finite-Element-Methods (FEM), implicit and explicit FEM-integration procedures for quasistatic applications, modeling of coupled thermo-mechanical problems, modeling of time dependent contact conditions, modeling of the nonlinear material behaviour, modeling of friction, FEM-based prediction of failure by means of cracks and crinkles.|
|Objective||Prozess optimization through numerical methods|
|Content||Application of virtual simulation methods for planning and optimization of metal-forming processes. Fundamentals of virtual simulation processes, based on Finite-Element-Methods (FEM) and Finite-Difference-Methods (FDM). Introduction to the basics of continuum and plasto mechanics to mathematically describe the plastic material flow of metals. The procedures to acquire process relevant features. The exercises include the application of industrial simulation tools for deep drawing in automotive applications, high pressure inner metal working (space frame) and rod extrusion.|
|151-0836-00L||Virtual Process Control in Forming Manufacturing Systems |
Does not take place this semester.
|W||5 credits||2V + 2U||P. Hora|
|Abstract||Introduction to the methods of virtual modeling of manufacturing processes, illustrated with examples from the digital automotive plant and others. The lecture presents an opportunity to learn the application of non-linear finite element analysis and optimization methods and also adresses stochastical methods for the control of the robust processes.|
|Objective||Integral study of virtual planning technologies in forming manufacturing systems|
|Content||Introduction to the methods of digital plant modeling. Examples: digital automitive plant, digital space-frame manufacturing, digital extrusion plant. Methods: virtual modeling of complex forming processes, non-linear FEA, optimization methods, stochastical methods.|
|151-0838-00L||Computational Methods in Micro- and Nano-Structures |
Does not take place this semester.
|W||5 credits||2V + 2U||P. Hora|
|Abstract||Fundamentals of computational modeling of micro- and nanostructures are treated, including the basics of molecular dynamics, microstructure scale crystal plasticity modeling and cellular automata methods. The different computational methods presented are taught with an emphasis on materials modeling.|
|Objective||Microstructures and especially nanostructures involve very few grains or even molecular layers. Conventional continuum mechanical modeling is no longer valid for these structures. This course treats computational methods, which include a description of material behavior at the microstructure scale, and can therefore be implemented in modeling micro- and nano-structures.|
|Content||Fundamentals of computational modeling of micro- and nanostructures are treated, indcluding the basics of molecular dynamics, microstructure scale crystal plasticity modelling and cellular automata methods. The different computational methods presented are taught with an emphasis on materials modeling.|
|151-0840-00L||Principles of FEM Based Optimization and Robustness Analysis||W||5 credits||2V + 2U||P. Hora, B. Berisha, N. Manopulo|
|Abstract||The course provides fundamentals of stochastic simulation and non-linear optimization methods. Methods of non-linear optimizaion for complex mechanical systems will be introduced und applied on real processes. Typical applications of stochastical methods for the prediction of process stability and robustness analysis will be discussed.|
|Objective||Real systems are, in general, of non-linear nature. Moreover, they are submitted to process parameter variations. In spite of this, most research is performed assuming deterministic boundary conditions, in which all parameters are constant. As a consequence, such research cannot draw conclusions on real system behavior, but only on behavior under singular conditions. Hence, the objective of this course is to give an insight into stochastic simulations and non-linear optimization methods. |
Students will learn mathematical methods e.g. gradient based and gradient free methods like genetic algorithm, and optimization tools (Matlab Optimization Toolbox) to solve basic optimization and stochastic problems.
Furthermore, special attention will be paid to the modeling of engineering problems using a commercial finite element program e.g. LS-Dyna to evaluate the mechanical response of a system, and an optimization tool e.g. LS-Opt for the mathematical optimization and robustness analysis.
|Content||Principles of nonlinear optimization|
- Introduction into nonlinear optimization and stochastic process simulation
- Principles of nonlinear optimization
- Introduction into the design optimization and probabilistic tool LS-Opt
- Design of Experiments DoE
- Introduction into nonlinear finite element methods
Optimization of nonlinear systems
- Application: Optimization of simple structures using LS-Opt and LS-Dyna
- Optimization based on meta modeling techniques
- Introduction into structure optimization
- Introduction into geometry parameterization for shape and topology optimization
Robustness and sensitivity of multiparameter systems
- Introduction into stochastics and robustness of processes
- Sensitivity analysis
- Application examples
|227-0224-00L||Stochastic Systems||W||4 credits||2V + 1U||J. Lygeros, F. Herzog|
|Abstract||Probability. Stochastic processes. Stochastic differential equations. Ito. Kalman filters. St Stochastic optimal control. Applications in financial engineering.|
|Objective||Stochastic dynamic systems. Optimal control and filtering of stochastic systems. Examples in technology and finance.|
|Content||- Stochastic processes|
- Stochastic calculus (Ito)
- Stochastic differential equations
- Discrete time stochastic difference equations
- Stochastic processes AR, MA, ARMA, ARMAX, GARCH
- Kalman filter
- Stochastic optimal control
- Applications in finance and engineering
|Lecture notes||H. P. Geering et al., Stochastic Systems, Measurement and Control Laboratory, 2007 and handouts|
|151-0206-00L||Energy Systems and Power Engineering||W||4 credits||2V + 2U||R. S. Abhari, A. Steinfeld|
|Abstract||Introductory first course for the specialization in ENERGY. The course provides an overall view of the energy field and pertinent global problems, reviews some of the thermodynamic basics in energy conversion, and presents the state-of-the-art technology for power generation and fuel processing.|
|Objective||Introductory first course for the specialization in ENERGY. The course provides an overall view of the energy field and pertinent global problems, reviews some of the thermodynamic basics in energy conversion, and presents the state-of-the-art technology for power generation and fuel processing.|
|Content||World primary energy resources and use: fossil fuels, renewable energies, nuclear energy; present situation, trends, and future developments. Sustainable energy system and environmental impact of energy conversion and use: energy, economy and society. Electric power and the electricity economy worldwide and in Switzerland; production, consumption, alternatives. The electric power distribution system. Renewable energy and power: available techniques and their potential. Cost of electricity. Conventional power plants and their cycles; state-of-the -art and advanced cycles. Combined cycles and cogeneration; environmental benefits. Solar thermal power generation and solar photovoltaics. Hydrogen as energy carrier. Fuel cells: characteristics, fuel reforming and combined cycles. Nuclear power plant technology.|
|Lecture notes||Vorlesungsunterlagen werden verteilt|
|151-0306-00L||Visualization, Simulation and Interaction - Virtual Reality I||W||4 credits||4G||A. Kunz|
|Abstract||Technology of Virtual Reality. Human factors, Creation of virtual worlds, Lighting models, Display- and acoustic- systems, Tracking, Haptic/tactile interaction, Motion platforms, Virtual prototypes, Data exchange, VR Complete systems, Augmented reality, Collaboration systems; VR and Design; Implementation of the VR in the industry; Human Computer Interfaces (HCI).|
|Objective||The product development process in the future will be characterized by the Digital Product which is the center point for concurrent engineering with teams spreas worldwide. Visualization and simulation of complex products including their physical behaviour at an early stage of development will be relevant in future. The lecture will give an overview to techniques for virtual reality, to their ability to visualize and to simulate objects. It will be shown how virtual reality is already used in the product development process.|
|Content||Introduction to the world of virtual reality; development of new VR-techniques; introduction to 3D-computergraphics; modelling; physical based simulation; human factors; human interaction; equipment for virtual reality; display technologies; tracking systems; data gloves; interaction in virtual environment; navigation; collision detection; haptic and tactile interaction; rendering; VR-systems; VR-applications in industry, virtual mockup; data exchange, augmented reality.|
|Lecture notes||A complete version of the handout is also available in English.|
|Prerequisites / Notice||Voraussetzungen:|
Vorlesung geeignet für D-MAVT, D-ITET, D-MTEC und D-INF
Testat/ Kredit-Bedingungen/ Prüfung:
– Teilnahme an Vorlesung und Kolloquien
– Erfolgreiche Durchführung von Übungen in Teams
– Mündliche Einzelprüfung 30 Minuten
|151-0314-00L||Information Technologies in the Digital Product||W||4 credits||3G||E. Zwicker, R. Montau|
|Abstract||Objective, Methods, Concepts of the Digital Product and Product-Life-Cycle-Management (PLM)|
Digital Product Fundamental: Productstructuring, Optimisation of Development- and Engineering Processes, Distribution and Use of Product Data in Sales, Production & Assembly, Service
PLM Fundamentals: Objects, Structures, Processes, Integrations
Application and Best Practices
|Objective||The students learn the basics and concepts of the product life cycle management (PLM), the usage of databanks, the integration of CAx-Systems, the configuration of computer networks and their protocols, moderne computer based communication (CSCW) or the variants and configuration management in regard to the creation, administration and usage of digital products.|
|Content||Möglichkeiten und Potentiale der Nutzung moderner IT-Tools, insbesondere moderner CAx- und PLM- Technologien. Der zielgerichtete Einsatz von CAx- und PLM-Technologien im Zusammenhang Produkt-Plattform - Unternehmensprozesse - IT-Tools. Einführung in die Konzepte des Produkt-Lifecycle-Managements (PLM): Informationsmodellierung, Verwaltung, Revisionierung, Kontrolle und Verteilung von Produktdaten bzw. Produkt-Plattformen. Detaillierter Aufbau und Funktionsweise von PLM-Systemen. Integration neuer IT-Technologien in bestehende und neu zu strukturierende Unternehmensprozesse. Möglichkeiten der Publikation und der automatischen Konfiguration von Produktvarianten auf dem Internet. Einsatz modernster Informations- und Kommunikationstechnologien (CSCW) beim Entwickeln von Produkten durch global verteilte Entwicklungszentren. Schnittstellen der rechnerintegrierten und unternehmensübergreifenden Produktentwicklung. Auswahl und Projektierung, Anpassung und Einführung von PLM-Systemen. Beispiele und Fallstudien für den industriellen Einsatz moderner Informationstechnologien.|
- Einführung in die PLM-Technologie
- Datenbanktechnologie im Digitalen Produkt
- Objektidentifikation mit Sachnummernsystem
- Prozess- Kooperationsmanagement
- Workflow Management
- Schnittstellen im Digitalen Produkt
- Enterprises Application Integration
|Lecture notes||Didaktisches Konzept/ Unterlagen/ Kosten|
Die Durchführung der Lehrveranstaltung erfolgt gemischt mit Vorlesungs- und Übungsanteilen anhand von Praxisbeispielen.
Handouts für Inhalt und Case; zT. E-learning; Kosten Fr.20.--
|Prerequisites / Notice||Voraussetzungen|
Informatik II; Fokus-Projekt; Freude an Informationstechnologien
Testat/ Kredit-Bedingungen / Prüfung
Erfolgreiche Durchführung von Übungen in Teams
Mündliche Prüfung 30 Minuten, theoretisch und anhand konkreter Problemstellungen
|151-0361-00L||Structural Analysis with FEM||W||4 credits||3G||G. Kress|
|Abstract||The class material includes mathematical ancillary concepts, derivation of element equations, boundary conditions, numerical integration, compilation of the system’s equations, solution methods, static and eigenvalue problems, sub-structuring techniques, degree-of-freedom coupling and non-linear simulation of progressing damage. ANSYS and also a MATLAB coded learning program are utilized.|
|Objective||With regard to structural analysis and simulation of Production processes, the theoretical background as well as practical abilities of an engineering analyst shall be transferred. The emphasis on optimization methods reflects the trend that computational methods are not only used to confirm the behaviour of exissting designs anymore but take an increasingliy active and creative role in the product development.|
|Content||1. Direct Method for Derivation of Finite Elements|
2. Variational Method for Derivation of Finite-Elements
3. Isoparametric Coordinate Transformation
4. Numerical Integration and Integration Errors
5. System equations Assembly
6. Boundary Conditions and Degree-of-Freedom Constraints
7. System equations Solution and Substructuring
8. Eigenvalue Problem Solution with Vector Iteration
9. Beam Elements and Locking Effect
10. Introduction to Application Software
|Lecture notes||Script and handouts are provided in class and can also be down-loaded from:|
|Literature||No textbooks required.|
|Prerequisites / Notice||Attestation requires doing and handing in of the homework assignments. Teaching language: English on request.|
|151-0940-00L||Modelling and Mathematical Methods in Process and Chemical Engineering||W||4 credits||3G||M. Mazzotti|
|Abstract||Study of the non-numerical solution of systems of ordinary differential equations and first order partial differential equations, with application to chemical kinetics, simple batch distillation, and chromatography.|
|Objective||Study of the non-numerical solution of systems of ordinary differential equations and first order partial differential equations, with application to chemical kinetics, simple batch distillation, and chromatography.|
|Content||Development of mathematical models in process and chemical engineering, particularly for chemical kinetics, batch distillation, and chromatography. Study of systems of ordinary differential equations (ODEs), their stability, and their qualitative analysis. Study of single first order partial differential equation (PDE) in space and time, using the method of characteristics. Application of the theory of ODEs to population dynamics, chemical kinetics (Belousov-Zhabotinsky reaction), and simple batch distillation (residue curve maps). Application of the method of characteristic to chromatography.|
|Lecture notes||no skript|
|Literature||A. Varma, M. Morbidelli, "Mathematical methods in chemical engineering," Oxford University Press (1997) |
H.K. Rhee, R. Aris, N.R. Amundson, "First-order partial differential equations. Vol. 1," Dover Publications, New York (1986)
R. Aris, "Mathematical modeling: A chemical engineer’s perspective," Academic Press, San Diego (1999)
|151-0119-00L||Molecular Fluid Mechanics||W||1 credit||1G||S. Schlamp, T. Rösgen|
|Abstract||Theory, applications, and simulation methods of fluids away from the continuum limit. The focus is on rarefied gases, but applications to micro-fluid mechanics will also be addressed.|
|Objective||Fluids are usually treated in the continuum limit. For example, this assumption underlies the Navier-Stokes equations. For certain applications, this is not appropriate; when either the gas becomes so dilute that the molecules' mean-free path is comparable to external length scales (such as for hypersonic flight in the upper atmosphere), or when the external length scales become so small as to approach the molecular length scales (microfluid mechanics).|
Students will learn:
- Relationship between the molecular nature of fluids and macroscopic quantities
- Underlying assumptions and approximations of continuum fluid mechanics in general and the Navier-Stokes equation in particular
- Theoretical and numerical approaches to treat non-continuum flows
|Content||Molecular description of matter: distribution functions, discrete-velocity gases, relation to macroscopic quantities|
Kinetic theory: free-path theory, internal degrees of freedom.
Boltzmann equation: BBGKY hierarchy and closure, H theorem, Euler equations, Chapman-Enskog procedure, free-molecule flows.
Collisionless and transitional flows
Direct simulation Monte Carlo methods
|Lecture notes||Printed lecture notes will be distributed in class.|
T. I. Gombosi , Gaskinetic Theory, Cambridge University Press, 2008.
Ching Shen, Rarefied Gas Dynamics: Fundamentals, Simulations and Micro Flows (Heat and Mass Transfer), Springer, Berlin, 2005.
|Prerequisites / Notice||At the class majority's request the lecture can be held in German; lecture notes, hoewever, will be in English in any case.|
|151-0182-00L||Theoretical and Applied Computational Fluid Dynamics||W||4 credits||3G||A. Haselbacher|
|Abstract||This course is focused on providing students with the knowledge and understanding required to develop simple computational fluid dynamics (CFD) codes and critically assess the results produced by CFD codes. As part of the course, students will develop their own code to solve the Euler and Navier-Stokes equations on unstructured grids and verify and validate them systematically.|
|Objective||Systematic introduction to development, analysis, and application of numerical methods for fluid-dynamics problems and interpretation of results.|
1. Governing and model equations. Brief review of equations and properties
2. Overview of basic concepts: Overview of discretization process and its consequences
3. Overview of numerical methods: Finite-difference, finite-volume, finite-element methods, spectral methods
4. Analysis of spatially discrete equations: Consistency, accuracy, stability, convergence of semi-discrete methods
5. Time-integration methods: LMS and RK methods, consistency, accuracy, stability, convergence
6. Analysis of fully discrete equations: Consistency, accuracy, stability, convergence of fully discrete methods
7. Solution of advection equation: One-dimensional advection equation, motivation for and consequences of upwinding, TVD and WENO methods, two-dimensional advection equation, multidimensional methods
8. Solution of Burgers equation: Non-linear stability, conservation, shock capturing, TVD and WENO methods
9. Solution of diffusion equation: Splitting and fractional step methods.
10. Numerical methods for compressible Euler equations: Riemann problem, Godunov's method, approximate Riemann solvers, non-reflecting boundary conditions
11. Numerical methods for incompressible Navier-Stokes equations: Incompressibility constraint and consequences, fractional-step and pressure-correction methods, artificial-compressibility method
|Lecture notes||The course is based mostly on notes developed by the instructor.|
|Literature||Literature: There is no required textbook. Suggested references are:|
1. R.J. Leveque, Finite Volume Methods for Hyperbolic Equations, Cambridge, 2002
2. E. F. Toro, Riemann Solvers and Numerical Methods for Fluid Dynamics, 3rd ed., Springer, 2009
3. H.K. Versteeg and W. Malalasekera, An Introduction to Computational Fluid Dynamics, 2nd ed., Pearson Prentice Hall, 2007
|Prerequisites / Notice||Prior knowledge of fluid dynamics, applied mathematics, basic numerical methods, and programming in Fortran and/or C++ (knowledge of MATLAB is *not* sufficient).|
|151-0980-00L||Biofluiddynamics||W||4 credits||2V + 1U||D. Obrist, P. Jenny|
|Abstract||Introduction to the fluid dynamics of the human body and the modeling of physiological flow processes (biomedical fluid dynamics).|
|Objective||A basic understanding of fluid dynamical processes of the human body. Knowledge of the basic concepts of fluid dynamics and the ability to apply these concepts appropriately.|
|Content||This lecture is an introduction to the fluid dynamics of the human body (biomedical fluid dynamics). For selected topics of human physiology, we introduce fundamental concepts of fluid dynamics (e.g., creeping flow, incompressible flow, flow in porous media, flow with particles, fluid-vessel interaction) and use them to model physiological flow processes. The list of studied topics includes the cardiovascular system and related diseases, respiratory fluiddynamics, fluiddynamics of the inner ear, blood rheology, microcirculation, and blood flow regulation.|
|Lecture notes||A script is provided in pdf-form.|
|Literature||A list of books on selected topics of biofluiddynamics will be provided.|
|227-0116-00L||VLSI I: From Architectures to VLSI Circuits and FPGAs||W||7 credits||5G||H. Kaeslin, N. Felber|
|Abstract||Understand Very-Large-Scale Integrated Circuits, Application-Specific Integrated Circuits, and Field-Programmable Gate-Arrays. Become fluent in their front-end design from architectural conception down to gate-level netlists. How to model and simulate digital circuits with VHDL. How to take advantage of automatic synthesis tools to produce industrial-quality circuits.|
|Objective||Understand Very-Large-Scale Integrated Circuits (VLSI chips), Application-Specific Integrated Circuits (ASIC), and Field-Programmable Gate-Arrays (FPGA). Know their organization and be able to identify suitable application areas. Become fluent in front-end design from architectural conception to gate-level netlists. How to model digital circuits with VHDL. How to ensure they behave as expected with the aid of simulation, testbenches, and assertions. How to take advantage of automatic synthesis tools to produce industrial-quality VLSI and FPGA circuits. Gain practical experience with the hardware description language VHDL and with industrial Electronic Design Automation (EDA) tools.|
|Content||This course is concerned with system-level issues of VLSI design and FPGA implementations. Topics include:|
- Overview on design methodologies and fabrication depths.
- Levels of abstraction for circuit modeling.
- VLSI and FPGA design flows.
- Dedicated and general purpose architectures compared.
- How to obtain an architecture for a given processing algorithm.
- Meeting throughput, area, and power goals by way of architectural transformations.
- Hardware Description Languages (HDL) and the underlying concepts.
- VHDL (IEEE standard 1076) for simulation and synthesis.
- A suitable nine-valued logic system (IEEE standard 1164).
- Register Transfer Level (RTL) synthesis and its limitations.
- Synchronous versus asynchronous circuits.
- The case for synchronous circuits.
- Periodic events and the Anceau diagram.
- Functional verification of digital integrated circuits.
- Modular and largely reusable testbenches.
- Assertion-based checks.
- Building blocks of digital VLSI circuits.
- Case studies, ASICs compared to microprocessors, DSPs, and FPGAs.
During the exercises, students learn how to model digital ICs with VHDL. They write testbenches for simulation purposes and synthesize gate-level netlists for VLSI chips and FPGAs. Only commercial EDA software by leading vendors is being used.
|Literature||"Digital Integrated Circuit Design, from VLSI Architectures to CMOS Fabrication" Cambridge University Press, 2008, ISBN 9780521882675.|
|Prerequisites / Notice||Prerequisites: |
Basics of digital circuits.
In written form following the course semester (spring term). Problems are given in English, answers will be accepted in either English oder German.
|227-0148-00L||VLSI III: Test and Fabrication of VLSI Circuits||W||6 credits||4G||N. Felber, H. Kaeslin|
|Abstract||Know how to apply methods, software tools and equipment for designing testable VLSI circuits, for testing fabricated ICs, and for physical analysis in the occurrence of defective parts. A basic understanding of modern semiconductor technologies.|
|Objective||Know how to apply methods, software tools and equipment for designing testable VLSI circuits, for testing fabricated ICs, and for physical analysis in the occurrence of defective parts. A basic understanding of modern semiconductor technologies.|
|Content||This final course in a series of three focusses on manufacturing, testing, physical analysis, and packaging of VLSI circuits. Topics include: |
- Effects of fabrication defects.
- Abstraction from physical to transistor- and gate-level fault models.
- Fault grading in the occurrence of large ASICs.
- Generation of efficient test vector sets.
- Enhancement of testability with built-in self test.
- Organisation and application of automated test equipment.
- Physical analysis of devices.
- Packaging problems and solutions.
- Models of industrial cooperation.
- The caveats of virtual components.
- The cost structures of ASIC development and manufacturing.
- Market requirements, decision criteria, and case studies.
- Today's deep-submicron CMOS fabrication processes.
- Outlook on the future evolution of semiconductor technology.
Exercises teach students how to use CAE/CAD software and automated equipment for testing ASICs after fabrication. Students that have submitted a design for manufacturing at the end of the 7th term do so on their own circuits. Physical analysis methods with professional equipment (AFM, DLTS) complement this training.
|Lecture notes||English lecture notes (Dr. N. Felber).|
|Literature||"Digital Integrated Circuit Design, from VLSI Architectures to CMOS Fabrication" Cambridge University Press, 2008, ISBN 9780521882675 (Dr. H. Kaeslin).|
|Prerequisites / Notice||Prerequisites: |
Basic knowledge of digital design.
|227-0418-00L||Algebra and Error Correcting Codes||W||6 credits||4G||H.‑A. Loeliger|
|Abstract||The course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course is also an introduction to "abstract" algebra and some of its applications in coding and signal processing.|
|Objective||The course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course is also an introduction to "abstract" algebra and some of its applications in coding and signal processing.|
|Content||Coding: coding and modulation, linear codes, Hamming space codes, Euclidean space codes, trellises and Viterbi decoding, convolutional codes, factor graphs and message passing algorithms, low-density parity check codes, turbo codes, Reed-Solomon codes.|
Algebra: groups, rings, homomorphisms, ideals, fields, finite fields, vector spaces, polynomials, Chinese Remainder Theorem.
|Lecture notes||Lecture Notes (english)|
|227-0420-00L||Information Theory II |
Does not take place this semester.
|W||6 credits||2V + 2U||A. Lapidoth|
|Abstract||This course builds on Information Theory I. It introduces additional topics in single-user communication, connections between Information Theory and Statistics, and Network Information Theory.|
|Objective||The course has two objectives: to introduce the students to the key information theoretic results that underlay the design of communication systems and to equip the students with the tools that are needed to conduct research in Information Theory.|
|Content||Differential entropy, maximum entropy, the Gaussian channel and water filling, the entropy-power inequality, Sanov's Theorem, Fisher information, the broadcast channel, the multiple-access channel, Slepian-Wolf coding, and the Gelfand-Pinsker problem.|
|Literature||T.M. Cover and J.A. Thomas, Elements of Information Theory, second edition, Wiley 2006|
|227-0434-00L||Harmonic Analysis: Theory and Applications in Advanced Signal Processing|
Does not take place this semester.
|W||6 credits||2V + 2U||H. Bölcskei|
|Abstract||Introduction to basic concepts in harmonic analysis with applications in signal processing and information theory.|
|Objective||Introduction to basic concepts in harmonic analysis with applications in signal processing and information theory.|
|Content||Elements of linear algebra, Fourier theory and sampling, Hilbert spaces, linear operator theory, frame theory, approximation theory, wavelets, short-time Fourier transform, Gabor expansion, filter banks, transform coding, sparse signals, uncertainty principles, compressed sensing.|
|Lecture notes||Lecture notes, problem sets with documented solutions.|
|Literature||S. Mallat, "A wavelet tour of signal processing", 2n ed., Academic Press, 1999 M. Vetterli and J. Kovacevic, "Wavelets and subband coding", Prentice Hall, 1995 I. Daubechies, "Ten lectures on wavelets", SIAM, 1992 O. Christensen, "An introduction to frames and Riesz bases", Birkhäuser, 2003 M. A. Pinksy, "Introduction to Fourier analysis and wavelets", Brooks/ Cole Series in Advanced Mathematics, 2002.|
|227-0104-00L||Communication and Detection Theory||W||6 credits||4G||A. Lapidoth|
|Abstract||This introduction to Detection and Communication Theory offers a glimpse at analog communication, but mainly focuses on the foundations of modern digital communications. Topics include the geometry of the space of energy-limited signals; the baseband representation of passband signals, spectral efficiency and the Nyquist Criterion; the power and power spectral density of PAM and QAM; hypothes|
|Objective||This is an introductory class to the field of wired and wireless communication. It offers a glimpse at classical analog modulation (AM, FM), but mainly focuses on aspects of modern digital communication, including modulation schemes, spectral efficiency, power budget analysis, block and convolu- tional codes, receiver design, and multi- accessing schemes such as TDMA, FDMA and Spread Spectrum.|
|Content||- Analog Modulation (AM, FM, DSB).|
- A block diagram of a digital cellular mobile phone system.
- The Nyquist Criterion for no ISI and the Matched Filter.
- Counting bits/dimension, bits/sec, bits/sec/Hz in base-band.
- Power Spectral Density, and the "energy- per-bit" parameter.
- Passband communication (QAM).
- Detection in white Gaussian noise.
- Sufficient statistics.
- The Chernoff and Bhattacharyya bounds.
- Signals as a vector space: continuous time Inner products and the Gram-Schmidt algorithm.
- Block and Convolutional Codes for the Gaussian channel.
- Multi-accessing schemes such as FDMA, TDMA, and CDMA
|Literature||A. Lapidoth, A Foundation in Digital Communication, Cambridge University Press 2009|
|227-0120-00L||Communication Networks||W||6 credits||4G||C. X. Dimitropoulos, K. A. Hummel, S. Neuhaus|
|Abstract||The students will understand the fundamental concepts of communication networks, with a focus on computer networking. They will learn to identify relevant mechanisms that are used in networks, and will see a reasonable set of examples implementing such mechanisms, both as seen from an abstract perspective and with hands-on, practical experience.|
|Objective||The students will understand the fundamental concepts of communication networks, with a focus on computer networking. They will learn to identify relevant mechanisms that are used to networks work, and will see a reasonable set of examples implementing such mechanisms, both as seen from an abstract perspective and with hands-on, practical experience.|
|Prerequisites / Notice||Prerequisites: A layered model of communication systems (represented by the OSI Reference Model) has previously been introduced.|
|227-0158-00L||Semiconductor Transport Theory and Monte Carlo Device Simulation||W||4 credits||2V + 1U||F. Bufler, A. Schenk|
|Abstract||The first part deals with semiconductor transport theory including the necessary quantum mechanics. |
In the second part, the Boltzmann equation is solved with the stochastic methods of Monte Carlo simulation.
The exercises address also TCAD simulations of MOSFETs. Thus the topics include theoretical physics,
numerics and practical applications.
|Objective||On the one hand, the link between microscopic physics and its concrete application in device simulation is established; on the other hand, emphasis is also laid on the presentation of the numerical techniques involved.|
|Content||Quantum theoretical foundations I (state vectors, Schroedinger and Heisenberg picture). Band structure (Bloch theorem, one dimensional periodic potential, density of states). Pseudopotential theory (crystal symmetries, reciprocal lattice, Brillouin zone).|
Semiclassical transport theory (Boltzmann transport equation (BTE), scattering processes, linear transport).<br>
Monte Carlo method (Monte Carlo simulation as solution method of the BTE, algorithm, expectation values).<br>
Implementational aspects of the Monte Carlo algorithm (discretization of the Brillouin zone, self-scattering according to Rees, acceptance- rejection method etc.). Bulk Monte Carlo simulation (velocity-field characteristics, particle generation, energy distributions, transport parameters). Monte Carlo device simulation (ohmic boundary conditions, MOSFET simulation).
Quantum theoretical foundations II (limits of semiclassical transport theory, quantum mechanical derivation of the BTE, Markov-Limes).
|Lecture notes||Lecture notes (in German)|
|252-0211-00L||Information Security||W||8 credits||4V + 3U||D. Basin, U. Maurer|
|Abstract||This course provides an introduction to Information Security. The focus|
is on fundamental concepts and models, basic cryptography, protocols and system security, and privacy and data protection. While the emphasis is on foundations, case studies will be given that examine different realizations of these ideas in practice.
|Objective||Master fundamental concepts in Information Security and their|
application to system building. (See objectives listed below for more details).
|Content||1. Introduction and Motivation (OBJECTIVE: Broad conceptual overview of information security) Motivation: implications of IT on society/economy, Classical security problems, Approaches to |
defining security and security goals, Abstractions, assumptions, and trust, Risk management and the human factor, Course verview. 2. Foundations of Cryptography (OBJECTIVE: Understand basic
cryptographic mechanisms and applications) Introduction, Basic concepts in cryptography: Overview, Types of Security, computational hardness, Abstraction of channel security properties, Symmetric
encryption, Hash functions, Message authentication codes, Public-key distribution, Public-key cryptosystems, Digital signatures, Application case studies, Comparison of encryption at different layers, VPN, SSL, Digital payment systems, blind signatures, e-cash, Time stamping 3. Key Management and Public-key Infrastructures (OBJECTIVE: Understand the basic mechanisms relevant in an Internet context) Key management in distributed systems, Exact characterization of requirements, the role of trust, Public-key Certificates, Public-key Infrastructures, Digital evidence and non-repudiation, Application case studies, Kerberos, X.509, PGP. 4. Security Protocols (OBJECTIVE: Understand network-oriented security, i.e.. how to employ building blocks to secure applications in (open) networks) Introduction, Requirements/properties, Establishing shared secrets, Principal and message origin authentication, Environmental assumptions, Dolev-Yao intruder model and
variants, Illustrative examples, Formal models and reasoning, Trace-based interleaving semantics, Inductive verification, or model-checking for falsification, Techniques for protocol design,
Application case study 1: from Needham-Schroeder Shared-Key to Kerberos, Application case study 2: from DH to IKE. 5. Access Control and Security Policies (OBJECTIVES: Study system-oriented security, i.e., policies, models, and mechanisms) Motivation (relationship to CIA, relationship to Crypto) and examples Concepts: policies versus models versus mechanisms, DAC and MAC, Modeling formalism, Access Control Matrix Model, Roll Based Access Control, Bell-LaPadula, Harrison-Ruzzo-Ullmann, Information flow, Chinese Wall, Biba, Clark-Wilson, System mechanisms: Operating Systems, Hardware Security Features, Reference Monitors, File-system protection, Application case studies 6. Anonymity and Privacy (OBJECTIVE: examine protection goals beyond standard CIA and corresponding mechanisms) Motivation and Definitions, Privacy, policies and policy languages, mechanisms, problems, Anonymity: simple mechanisms (pseudonyms, proxies), Application case studies: mix networks and crowds. 7. Larger application case study: GSM, mobility
|252-0526-00L||Statistical Learning Theory||W||4 credits||2V + 1U||J. M. Buhmann|
|Abstract||The course covers advanced methods of statistical learning :|
PAC learning and statistical learning theory;variational methods and optimization, e.g., maximum entropy techniques, information bottleneck, deterministic and simulated annealing; clustering for vectorial, histogram and relational data; model selection; graphical models.
|Objective||The course surveys recent methods of statistical learning. The fundamentals of machine learning as presented in the course "Introduction to Machine Learning" are expanded and in particular, the theory of statistical learning is discussed.|
|Content||# Boosting: A state-of-the-art classification approach that is sometimes used as an alternative to SVMs in non-linear classification.|
# Theory of estimators: How can we measure the quality of a statistical estimator? We already discussed bias and variance of estimators very briefly, but the interesting part is yet to come.
# Statistical learning theory: How can we measure the quality of a classifier? Can we give any guarantees for the prediction error?
# Variational methods and optimization: We consider optimization approaches for problems where the optimizer is a probability distribution. Concepts we will discuss in this context include:
* Maximum Entropy
* Information Bottleneck
* Deterministic Annealing
# Clustering: The problem of sorting data into groups without using training samples. This requires a definition of ``similarity'' between data points and adequate optimization procedures.
# Model selection: We have already discussed how to fit a model to a data set in ML I, which usually involved adjusting model parameters for a given type of model. Model selection refers to the question of how complex the chosen model should be. As we already know, simple and complex models both have advantages and drawbacks alike.
# Reinforcement learning: The problem of learning through interaction with an environment which changes. To achieve optimal behavior, we have to base decisions not only on the current state of the environment, but also on how we expect it to develop in the future.
|Lecture notes||no script; transparencies of the lectures will be made available.|
|Literature||Duda, Hart, Stork: Pattern Classification, Wiley Interscience, 2000.|
Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001.
L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996
|Prerequisites / Notice||Requirements: |
basic knowledge of statistics, interest in statistical methods.
It is recommended that Introduction to Machine Learning (ML I) is taken first; but with a little extra effort Statistical Learning Theory can be followed without the introductory course.
|252-0570-00L||Game Programming Laboratory|
In the Master Programme max. 10 credits can be accounted by Labs
on top of the Interfocus Courses. Additional Labs will be listed on the Addendum.
|W||10 credits||9P||B. Sumner|
|Abstract||The goal of this course is the in-depth understanding of the technology and programming underlying computer games. Students gradually design and develop a computer game in small groups and get acquainted with the art of game programming.|
|Objective||The goal of this new course is to acquaint students with the|
technology and art of programming modern three-dimensional computer
|Content||This is a new course that addresses modern three-dimensional computer|
game technology. During the course, small groups of students will
design and develop a computer game. Focus will be put on technical
aspects of game development, such as rendering, cinematography,
interaction, physics, animation, and AI. In addition, we will
cultivate creative thinking for advanced gameplay and visual effects.
The "laboratory" format involves a practical, hands-on approach with
neither traditional lectures nor exercises. Instead, we will meet
once a week to discuss technical issues and to track progress. We
plan to utilize Microsoft's XNA Game Studio Express, which is a
collection libraries and tools that facilitate game development.
While development will take place on PCs, we will ultimately deploy
our games on the XBox 360 console.
At the end of the course we will present our results to the public.
|Lecture notes||Online XNA documentation.|
|Prerequisites / Notice||The number of participants is limited.|
- good programming skills (Java, C++, C#, etc.)
- CG experience: Students should have taken, at a minimum, Visual
Computing. Higher level courses are recommended, such as Introduction
to Computer Graphics, Surface Representations and Geometric Modeling,
and Physically-based Simulation in Computer Graphics.
|252-0504-00L||Numerical Methods for Solving Large Scale Eigenvalue Problems||W||4 credits||3G||P. Arbenz|
|Abstract||In this lecture algorithms are investigated for solving eigenvalue problems|
with large sparse matrices. Some of these eigensolvers have been developed
only in the last few years. They will be analyzed in theory and practice (by means
of MATLAB exercises).
|Objective||Knowing the modern algorithms for solving large scale eigenvalue problems, their numerical behavior, their strengths and weaknesses.|
|Content||The lecture starts with providing examples for applications in which|
eigenvalue problems play an important role. After an introduction
into the linear algebra of eigenvalue problems, an overview of
methods (such as the classical QR algorithm) for solving small to
medium-sized eigenvalue problems is given.
Afterwards, the most important algorithms for solving large scale,
typically sparse matrix eigenvalue problems are introduced and
analyzed. The lecture will cover a choice of the following topics:
* vector and subspace iteration
* trace minimization algorithm
* Arnoldi and Lanczos algorithms (including restarting variants)
* Davidson and Jacobi-Davidson Algorithm
* preconditioned inverse iteration and LOBPCG
* methods for nonlinear eigenvalue problems
In the exercises, these algorithm will be implemented (in simplified forms)
and analysed in MATLAB.
|Lecture notes||Lecture notes, |
Copies of slides
|Literature||Z. Bai, J. Demmel, J. Dongarra, A. Ruhe, and H. van der Vorst: Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide. SIAM, Philadelphia, 2000.|
Y. Saad: Numerical Methods for Large Eigenvalue Problems. Manchester University Press, Manchester, 1994.
G. H. Golub and Ch. van Loan: Matrix Computations, 3rd ed. Johns Hopkins University Press, Baltimore 1996.
|Prerequisites / Notice||Prerequisite: linear agebra|
|252-5101-00L||Numerical Simulation of Dynamic Systems||W||4 credits||2V + 1U||F. E. Cellier|
|Abstract||Numerical Simulation of Dynamic Systems teaches the numerical solution to differential equation (ODE, PDE, DAE) system descriptions as they result from modeling physical and engineering systems.|
|Objective||The students learn a broad spectrum of algorithms for the numerical solution of implicitly formulated differential and algebraic equation (DAE) systems, as they commonly result from the derivation of mathematical descriptions of physical and engineering systems. Although the techniques taught in this class are techniques of applied mathematics, the emphasis of the class is one of engineering systems design. The students learn how to simulate across discontinuities, as these are present in most models of engineering systems, such as in power electronics. The students are being taught how to deal with higher index DAE models, as they are derived frequently, e.g. from mechanical multi-body systems. The students learn further how to synchronize the simulation clock with physical time for the purpose of real-time simulations of systems, possibly with hardware in the loop. Finally, they are taught how to distribute simulations over multiple processors, while minimizing the inter-processor communication overhead.|
|Content||The class Numerical Simulation of Dynamic Systems (NSDS) teaches the students how to compute the trajectory behavior of implicitly formulated differential and algebraic equation (DAE) systems, as they commonly result from the derivation of mathematical descriptions of physical and engineering systems. NSDS is the sister class of the class Mathematical Modeling of Physical Systems (MMPS), in which the students learn how to derive mathematical descriptions of physical systems. MMPS is offered annually in the fall semester.|
|Lecture notes||Presentations of all lectures will be published on the web.|
|Literature||Cellier, F.E. and E. Kofman (2006), Continuous System Simulation, Springer-Verlag, New York, ISBN 0-387-26102-8, 643p.|
|252-1426-00L||Approximation Algorithms and Semidefinite Programming||W||7 credits||3V + 2U + 1A||B. Gärtner, J. Matousek|
|Abstract||Over the last fifteen years, semidefinite programming has become an important tool for approximate solutions of hard combinatorial problems. In this lecture, we introduce the foundations of semidefinite programming, we present some of its applications in (but not only in) approximation algorithms, and we show how semidefinite programs can efficiently be solved.|
|Objective||Students should understand that semidefinite programs form a well-understood class of optimization problems that can (approximately) be solved in polynomial time and yet are powerful enough to yield good approximate solutions for hard combinatorial problems.|
|Content||The Goemans-Williamson MAXCUT algorithm. semidefinite programming, The Lovasz theta function, cone programming and duality, algorithms for semidefinite programming, advanced applications of semidefinite programming in approximation algorithms|
|Lecture notes||The lecture will follow (parts of) the book "Approximation Algorithms and Semidefinite Programming" by the lecturers (see literature).|
|Literature||Bernd Gärtner and Jiri Matousek: Approximation Algorithms and Semidefinite Programming, Springer, 2012|
David P. Williamson and David B. Shmoys: The Design of Approximation Algorithms, Cambridge University Press, 2011
|Prerequisites / Notice||Basic knowledge in linear algebra and analysis; the ability to fill in routine details in proofs;|
|252-0564-00L||Scientific Visualization||W||4 credits||2V + 1U||R. Peikert|
|Abstract||Scientific visualization is the application of computer graphics to the visual analysis and interactive exploration of scientific data which have typically spatial or spatio-temporal domain. Such datasets arise in engineering, natural and medical sciences, and are generated by simulation, measurement or imaging techniques.|
|Objective||Becoming familiar with the fundamental methods and some advanced techniques of scientific visualization. Being able to apply visualization to measurement or simulation data and to correctly interpret visualization results.|
|Content||This course covers advanced topics in Scientific Visualization, including: contouring and isosurfaces, direct volume rendering, visualization of flow and vector fields, texture advection, feature extraction, topological methods, information visualization, visualization software, and hot topics of current research.|
|252-0538-00L||Shape Modeling and Geometry Processing||W||4 credits||2V + 1U||O. Sorkine Hornung|
|Abstract||This course covers some of the latest developments in geometric modeling and digital geometry processing. Topics include surface modeling based on triangle meshes, mesh generation, surface reconstruction, subdivision schemes, mesh fairing and simplification, discrete differential geometry and interactive shape editing.|
|Objective||The students will learn how to design, program and analyze algorithms and systems for interactive 3D shape modeling and digital geometry processing.|
|Content||Recent advances in 3D digital geometry processing have created a plenitude of novel concepts for the mathematical representation and interactive manipulation of geometric models. This course covers some of the latest developments in geometric modeling and digital geometry processing. Topics include surface modeling based on triangle meshes, mesh generation, surface reconstruction, subdivision schemes, mesh fairing and simplification, discrete differential geometry and interactive shape editing.|
|Lecture notes||Slides and course notes|
|Prerequisites / Notice||Prerequisites:|
Introduction to Computer Graphics, experience with C++ programming. Some background in geometry or computational geometry is helpful, but not necessary.
|252-0579-00L||3D Photography||W||4 credits||3G||M. Pollefeys, K. Köser|
|Abstract||The goal of this course is to provide students with a good understanding of how 3D object shape and appearance can be estimated from images and videos. The main concepts and techniques will be studied in depth and practical algorithms and approaches will be discussed and explored through the exercises and a course project.|
|Objective||After attending this course students should:|
1. Understand the concepts that allow recovering 3D shape from images.
2. Have a good overview of the state of the art in 3D photography
3. Be able to critically analyze and asses current research in the area
4. Implement components of a 3D photography system.
|Content||The course will cover the following topics a.o. camera model and calibration, single-view metrology, triangulation, epipolar and multi-view geometry, two-view and multi-view stereo, structured-light, feature tracking and matching, structure-from-motion, shape-from-silhouettes and 3D modeling and applications.|
|252-0312-00L||Ubiquitous Computing||W||3 credits||2V||F. Mattern|
|Abstract||Ubiquitous computing integrates tiny wirelessly connected computers and sensors into the environment and everyday objects. Main topics: The vision of ubiquitous computing, trends in technology, smart cards, RFID, Bluetooth, sensor networks, location awareness, application areas and business issues, privacy.|
|Objective||The vision of ubiquitous computing, trends in technology, smart cards, RFID, Bluetooth, sensor networks, location awareness, application areas and business issues, privacy.|
|Lecture notes||Copies of slides will be made available|
|Literature||Will be provided in the lecture. To put you in the mood:|
Mark Weiser: The Computer for the 21st Century. Scientific American, September 1991, pp. 94-104
|263-2300-00L||How To Write Fast Numerical Code||W||6 credits||3V + 2U||M. Püschel|
|Abstract||This course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for numerical functionality such as linear algebra and others. The focus is on optimizing for the memory hierarchy and for special instruction sets. Finally, the course will introduce the recent field of automatic performance tuning.|
|Objective||Software performance (i.e., runtime) arises through the interaction of algorithm, its implementation, and the microarchitecture the program is run on. The first goal of the course is to provide the student with an understanding of this interaction, and hence software performance, focusing on numerical or mathematical functionality. The second goal is to teach a general systematic strategy how to use this knowledge to write fast software for numerical problems. This strategy will be trained in a few homeworks and semester-long group projects.|
|Content||The fast evolution and increasing complexity of computing platforms pose a major challenge for developers of high performance software for engineering, science, and consumer applications: it becomes increasingly harder to harness the available computing power. Straightforward implementations may lose as much as one or two orders of magnitude in performance. On the other hand, creating optimal implementations requires the developer to have an understanding of algorithms, capabilities and limitations of compilers, and the target platform's architecture and microarchitecture. |
This interdisciplinary course introduces the student to the foundations and state-of-the-art techniques in high performance software development using important functionality such as linear algebra functionality, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and, if time permits, how to write multithreaded code for multicore platforms. Much of the material is based on state-of-the-art research.
Further, a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course. Finally, the course will introduce the students to the recent field of automatic performance tuning.
|401-3901-00L||Mathematical Optimization||W||6 credits||2V + 1U||R. Weismantel|
|Abstract||Mathematical treatment of diverse optimization techniques.|
|Objective||Advanced optimization theory and algorithms.|
|Content||1. Mixed integer optimization models: Geometry and basic examples.|
2. Discrete optimization technique: 0/1-lift and project, cutting plane theory and proximity of integer and continuous points.
3. Combinatorial optimization: Basic concepts of complexity theory (notions of P, NP and NP-complete), optimization problems in graphs, polynomial combinatorial algorithms, integrality of polyhedra.
4. Nonlinear optimization: Basic concepts and algorithms for unconstrained optimization (descent methods, conjugate gradient and (Quasi-) Newton method) with convergence analysis for the convex case, Lagrange and Kuhn-Tucker theory
|Prerequisites / Notice||This course assumes the basic knowledge of linear programming, which is taught in courses such as "Introduction to Optimization" (401-2903-00L).|
|401-3908-09L||Polyhedral Computation||W||6 credits||2V + 1U||K. Fukuda|
|Abstract||Polyhedral computation deals with various computational problems associated with convex polyhedra in general dimension. Typical problems include the representation conversion problem (between halfspace and generator representations), the polytope volume computation, the construction of hyperplane arrangements and zonotopes, the Minkowski addition of convex polytopes.|
|Content||In this lecture, we study basic and advanced techniques for polyhedral computation in general dimension. We review some classical results on convexity and convex polyhedra such as polyhedral duality, Euler's relation, shellability, McMullen's upper bound theorem, the Minkowski-Weyl theorem, face counting formulas for arrangements, Shannon's theorem on simplicial cells. Our main goal is to investigate fundamental problems in polyhedral computation from both the complexity theory and the viewpoint of algorithmic design. Optimization methods, in particular, linear programming algorithms, will be used as essential building blocks of advanced algorithms in polyhedral computation. Various research problems, both theoretical and algorithmic, in polyhedral computation will be presented.|
We also study applications of polyhedral computation in combinatorial optimization, integer programming, game theory, parametric linear and quadratic programming.
|Lecture notes||Lecture notes will be posted as pdf file.|
|Prerequisites / Notice||This course assumes the basic knowledge of linear programming, which is taught in courses such as "Mathematical Optimization" (401-3901-00L) and "Introduction to Optimization" (401-2903-00L).|
|401-3904-00L||Convex Optimization||W||6 credits||2V + 1U||M. Baes|
|Abstract||The course "Convex optimization" encompasses in a balanced manner theory (convex analysis, duality theory, optimality conditions), applications, and algorithms for convex optimization.|
|Objective||The aim of this course is to give to mathematicians and practitioners an overview of useful concepts and techniques in convex optimization. A particular attention is given to convex modeling and to algorithms for solving convex optimization problems. Some exercise sessions are devoted to an initiation to a convex optimization solver.|
In summary, we will discuss one of the most challenging research areas of nonlinear optimization for which there are many interesting open questions both in theory and practice.
Here is a brief syllabus of the course.
* Mathematical background (6 lectures)
Introduction, convex sets, Semidefinite cone, separation theorems,
Duality, Farkas Lemma, Optimality conditions, Lagrangian duality,
Subgradients, conjugate functions, KKT conditions and applications.
*Applications, convex modeling (3 lectures)
Conic Optimization and applications,
Applications of Semidefinite Optimization
Applications of Convex Optimization to Data Fitting and Statistical
*Algorithms (5 lectures)
Black-box methods, Self-concordant functions,
Interior-point methods, Primal-dual interior-point methods.
|Content||Convexity plays a central role in the design and analysis of modern and highly successful algorithms for solving real-world optimization problems. The lecture (in English) on convex optimization will treat in a balanced manner theory (convex analysis, optimality conditions), modeling issues, and algorithms for convex optimization. Beginning with basic concepts and results about the structure of convex sets, continuity and differentiability of convex functions (including conjugate functions), the lecture will cover separation theorems and their important consequences: the theory of Lagrange multipliers, the duality theory and some min-max theorems.|
On the algorithmic part, the course will study some simple first and second-order algorithms, as well as some efficient interior-point methods in the framework of self-concordant functions.
|Lecture notes||The slides of the course are available online, on the course website. An important reference book for the lecture is "S. Boyd, L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004", available online for free.|
|Literature||* A. Barvinok, A Course in Convexity. American Mathematical Society, 2003.|
* A. Ben-Tal and A. Nemirovski, Lectures on Modern Convex Optimization - Analysis, Algorithms, and Engineering Applications, MPS-SIAM Series on Optimization, MPS-SIAM.
* D. P. Bertsekas, A. Nedic and A. E. Ozdaglar, Convex Analysis and Optimization, Athena Scientific, 2003.
* D. Bertsimas and J. N. Tsitsiklis, Introduction to Linear Optimization, Athena Scientific, 1997.
* S. Boyd, L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004.
* S. Boyd, L. El Ghaoui, E. Feron and V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory. SIAM, 1994.
* E. de Klerk, Aspects of Semidefinite Programming: Interior Point Algorithms and Selected Applications, Book Series: APPLIED OPTIMIZATION, Vol. 65. Kluwer Academic Publishers.
* Y. Nesterov, Introductory Lectures on Convex Optimization: A Basic Course, Book Series: APPLIED OPTIMIZATION, Vol. 87. Kluwer Academic Publishers,
* R. A. Horn and C. R. Johnson, Matrix Analysis, Cambridge University Press, 1985.
* J. Renegar, A Mathematical View of Interior-Point Methods in Convex Optimization, MPS-SIAM Series on Optimization.
* H. Wolkowicz, R. Saigal and L. Vandenberghe, Handbook of Semidefinite Programming: Theory, Algorithms, and Applications, Kluwer Academic Publishers.
* A. Nemirovski and D. Yudin, Problem complexity and method efficiency in optimization, Wiley, 1983.
|401-4606-00L||Numerical Analysis of Stochastic Partial Differential Equations||W||8 credits||4G||A. Barth, A. Lang|
|Abstract||Mathematical formulation of partial differential equations with random inputs,|
and numerical analysis of deterministic approximation
methods for them:
Karhunen-Loeve expansion of random fields, measures on Hilbert spaces,
multilevel Finite Element methods, sparse tensor and polynomial chaos type approximation methods
|Objective||The mathematical formulation of stochastic and random partial |
differential equations and the main discretization methods.
1.1 Functional analysis
1.2 Probability theory
2 Stochastic partial diffrential equations
2.1 Gaussian measures
2.2 Wiener processes
2.3 Stochastic integration
2.4 Solutions of stochastic partial differential equations
2.5 Finite Element approximation
2.6 Noise approximation
2.7 (Multilevel) Monte Carlo methods
3 Random partial differential equations
3.1 Distributions on Banach spaces
3.2 Elliptic partial differential equation with stochastic right hand
3.2.1 Existence and uniqueness
3.2.2 Finite Element method
3.2.3 Full and sparse tensor approximations
3.3 Elliptic partial differential equation with stochastic operator
3.3.1 Existence and uniqueness
3.3.2 Finite Element method
3.3.3 (Multilevel) Monte Carlo methods
3.3.4 Stochastic Galerkin methods
|Lecture notes||No lecture notes but handouts on selected topics will be provided.|
|Literature||1. Stochastic Equations in Infinite Dimensions|
G. Da Prato and J. Zabczyk
Cambridge Univ. Press (1992)
2. Taylor Approximations for Stochastic Partial Differential Equations
A. Jentzen and P.E. Kloeden
3. Numerical Solution of Stochastic Differential Equations
P.E. Kloeden and E. Platen
Springer Verlag (1992)
4. A Concise Course on Stochastic Partial Differential Equations
C. Prévôt and M. Röckner
Springer Verlag (2007)
5. Galerkin Finite Element Methods for Parabolic Problems
Springer Verlag (2006)
|Prerequisites / Notice||Functional analysis, numerical solution of elliptic and parabolic PDEs, probability theory, stochastic processes|
|402-0577-00L||Quantum Systems for Information Technology||W||8 credits||2V + 2U||S. Filipp|
|Abstract||Introduction to experimental quantum information processing (QIP). Quantum bits. Coherent Control. Quantum Measurement. Decoherence. Microscopic and macroscopic quantum systems. Nuclear magnetic resonance (NMR) in molecules and solids. Ions and neutral atoms in electromagnetic traps. Charges and spins in quantum dots. Charges and flux quanta in superconducting circuits. Novel hybrid systems.|
|Objective||In recent years the realm of quantum mechanics has entered the domain of information technology. Enormous progress in the physical sciences and in engineering and technology has allowed us to envisage building novel types of information processors based on the concepts of quantum physics. In these processors information is stored in the quantum state of physical systems forming quantum bits (qubits). The interaction between qubits is controlled and the resulting states are read out on the level of single quanta in order to process information. Realizing such challenging tasks may allow constructing an information processor much more powerful than a classical computer. The aim of this class is to give a thorough introduction to physical implementations pursued in current research for realizing quantum information processors. The field of quantum information science is one of the fastest growing and most active domains of research in modern physics.|
|Content||A syllabus will be provided on the class web server at the beginning of the term (see section 'Besonderes'/'Notice').|
|Lecture notes||Electronically available lecture notes will be published on the class web server (see section 'Besonderes'/'Notice').|
|Literature||Quantum computation and quantum information / Michael A. Nielsen & Isaac L. Chuang. Reprinted. Cambridge : Cambridge University Press ; 2001.. 676 p. : ill.. .|
Additional literature and reading material will be provided on the class web server (see section 'Besonderes'/'Notice').
|Prerequisites / Notice||The class will be taught in English language.|
Basic knowledge of quantum mechanics is required, prior knowledge in atomic physics, quantum electronics, and solid state physics is advantageous.
More information on this class can be found on the web site: http://www.solid.phys.ethz.ch/wallraff/content/courses/coursesmain.html
|402-0472-00L||Mesoscopic Quantum Optics|
Does not take place this semester.
|W||8 credits||3V + 1U||A. Imamoglu|
|Abstract||Description of open quantum systems using quantum trajectories. Cascaded quantum systems. Decoherence and quantum measurements. Elements of single quantum dot spectroscopy: interaction effects. Spin-reservoir coupling.|
|Objective||This course covers basic concepts in mesoscopic quantum optics and builds up on the material covered in the Quantum Optics course. The specific topics that will be discussed include emitter-field interaction in the electric-dipole limit, spontaneous emission, density operator and the optical Bloch equations, quantum optical phenomena in quantum dots (photon antibunching, cavity-QED) and confined spin dynamics.|
|Content||Description of open quantum systems using quantum trajectories. Cascaded quantum systems. Decoherence and quantum measurements. Elements of single quantum dot spectroscopy: interaction effects. Spin-reservoir coupling.|
|Lecture notes||Y. Yamamoto and A. Imamoglu, "Mesoscopic Quantum Optics," (Wiley, 1999).|
|402-0804-00L||Neuromorphic Engineering II||W||6 credits||5G||T. Delbrück, G. Indiveri, S.‑C. Liu|
|Abstract||This course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the fall semester course "Neuromorphic Engineering I".|
|Objective||Design of a neuromorphic circuit for implementation with CMOS technology.|
|Content||This course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the autumn semester course "Neuromorphic Engineering I".|
The principles of CMOS processing technology are presented. Using a set of inexpensive software tools for simulation, layout and verification, suitable for neuromorphic circuits, participants learn to simulate circuits on the transistor level and to make their layouts on the mask level. Important issues in the layout of neuromorphic circuits will be explained and illustrated with examples. In the latter part of the semester students simulate and layout a neuromorphic chip. Schematics of basic building blocks will be provided. The layout will then be fabricated and will be tested by students during the following fall semester.
|Literature||S.-C. Liu et al.: Analog VLSI Circuits and Principles; software documentation.|
|Prerequisites / Notice||Prerequisites: Neuromorphic Engineering I strongly recommended|
|402-0816-00L||Computational Physics and Econophysics||W||5 credits||2V + 2U||D. Würtz|
|Abstract||Introduction to principles of computational finance and financial engineering from an econophysicist point of view. Prerequisite R/SPlus programming.|
|Objective||Introducing main statistical methods for numerical modelling of financial|
time series, valuation of derivatives, and optimization of portfolios.
Implementing numerical methods using the statistical software environment R.
|Content||- Overview on R/Rmetrics and SPlus/Finmetrics.|
- Financial Returns, Stylized Facts, Stable and Hyperbolic Distributions
- ARMA and GARCH Time Series Modelling, Trends and Unit Roots
- Technical Analysis, Trading Models and Decision Making
- Extreme Value Theory and Dependence Structures (Copulae)
- Plain Vanilla and Exotic Option Pricing, Monte Carlo Simulations
- Markowitz and CVaR Portfolio Optimization
|Lecture notes||Lecture notes written in English as well as R/Rmetrics software for|
registered participants in the course.
|402-0806-00L||Computational Vision||W||6 credits||2V + 1U||R. J. Douglas, D. Kiper, K. A. Martin|
|Abstract||This course focuses on neural computations that underlie visual perception. We study how visual signals are processed in the retina, LGN and visual cortex. We study the morpholgy and functional architecture of cortical circuits responsible for pattern, motion, color, and three-dimensional vision.|
|Objective||This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. |
The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will
be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
|Content||This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. |
The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will
be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
|Literature||Books: (recommended references, not required)|
1. An Introduction to Natural Computation, D. Ballard (Bradford Books, MIT Press) 1997.
2. The Handbook of Brain Theorie and Neural Networks, M. Arbib (editor), (MIT Press) 1995.
|402-0981-00L||Computer Simulations of Sensory Systems||W||3 credits||2V + 1U||T. Haslwanter|
|Abstract||This course deals with computer simulations of the human auditory, visual, and balance system. The lecture will cover the physiological and mechanical mechanisms of these sensory systems. And in the exercises, the simulations will be implemented with MATLAB. The simulations will be such that their output could be used as input for actual neuro-sensory prostheses.|
|Objective||Our sensory systems provide us with information about what is happening in the world surrounding us. Thereby they transform incoming mechanical, electromagnetic, and chemical signals into “action potentials”, the language of the central nervous system.|
The main goal of this lecture is to describe how our sensors achieve these transformations, how they can be reproduced with computational tools, and how they can be implemented using MATLAB. For example, our auditory system performs a “Fourier transformation” of the incoming sound waves; our early visual system is optimized for finding edges in images that are projected onto our retina; and our balance system can be well described with a “control system” that transforms linear and rotational movements into nerve impulses.
In the exercises that go with this lecture, we will use MATLAB toolboxes to reproduce the transformations achieved by our sensory systems. The goal is to write programs whose output could be used as input for actual neurosensory prostheses: such prostheses have become commonplace for the auditory system, and are under development for the visual and the balance system.
|Content||The following topics will be covered:|
• Introduction into the signal processing in nerve cells.
• Introduction into MATLAB.
• Simplified simulation of nerve cells (Hodgkins-Huxley model).
• Description of the auditory system, including the application of Fourier transforms on recorded sounds.
• Description of the visual system, including the retina and the information processing in the visual cortex. The corresponding exercises will provide an introduction to digital image processing.
• Description of the mechanics of our balance system, and the “Control System”-language that can be used for an efficient description of the corresponding signal processing (essentially Laplace transforms, and the “Simulink” module of MATLAB).
|Lecture notes||For each module an English script will be provided on the e-learning platform "moodle". The main content of the lecture is also available as a wikibook, under http://en.wikibooks.org/wiki/Biological_Machines|
|Literature||Open source information is available as wikibook http://en.wikibooks.org/wiki/Biological_Machines|
For good overviews I recommend:
• L. R. Squire, D. Berg, F. E. Bloom, Lac S. du, A. Ghosh, and N. C. Spitzer. Fundamental Neuroscience, Academic Press - Elsevier, 2008 [ISBN: 978-0-12-374019-9].
This book covers the biological components, from the functioning of an individual ion channels through the various senses, all the way to consciousness. And while it does not cover the computational aspects, it nevertheless provides an excellent overview of the underlying neural processes of sensory systems. More technical and harder to read than “Kandel/Schwartz – Principles of Neural Sciences”, but therefore much more up-to-date.
• P Wallisch, M Lusignan, M. Benayoun, T. I. Baker, A. S. Dickey, and N. G. Hatsopoulos. MATLAB for Neuroscientists, Academic Press, 2009.
Compactly written, it provides a short introduction to MATLAB, as well as a very good overview of MATLAB’s functionality, focusing on applications in different areas of neuroscience.
• G. Mather. Foundations of Perception, Psychology Press, 2006 [ISBN: 0-86377-834-8 (hardcover), oder 0-86377-835-6 (paperback)]
A coherent, up-to-date introduction to the basic facts and theories concerning human sensory perception.
|Prerequisites / Notice||Since I have to gravel from Linz, Austria, to Zurich to give this lecture, I plan to hold this lecture in blocks (every 2nd week).|
|402-0738-00L||Statistical Methods and Analysis Techniques in Experimental Physics||W||6 credits||2V + 3U||C. Grab, M. Donegà, C. Regenfus|
|Abstract||This lecture focuses on state-of-the-art statistical methods of the type employed for data analysis in experimental physics. In the practical exercises, students perform independent analyses on the computer using data taken from genuine experiments. Examples and real data are taken from particle physics topics.|
|Objective||Getting to know the methods and tools and learning the necessary skills to analyse large data records in a statistically correct manner. Learning how to present scientific results in a professional manner and how to discuss them.|
- Moderne Methoden der statistischen Datenanalyse.
- Wahrscheinlichkeitsverteilungen, Fehlerrechnung, Simulationsmethoden, Schätzmethoden, Blindstudien
- Monte Carlo methoden,Konfidenzintervalle, Hypothesentests, Regularisierung, Entfaltung, Moderne multivariate Methoden
- Viele Beispiele aus der Teilchenphysik.
- Vorlesung zu theoretischen Grundlagen.
- Gemeinsame Diskussion von Musterbeispielen;
- Uebungen: spezifische Aufgaben, um das in der VL Behandelte zu vertiefen.
- Die Studierenden fuehren statistische Modell-Rechnungen mithilfe eines ausgewaehlten Programms selbst am Computer durch.
- Gruppenarbeit (zu zweit): Durchfuehren einer eigenen Datenanalyse mit reellen Daten, die aus aktuellen Forschungsprojekten stammen.
- Studierende stellen ihre Arbeiten am Ende vor in einem wissenschaftlichen Vortrag mit Diskussion.
- Direkte Betreuung der Studierenden durch Assistierende waehrend ihrer Auswertearbeit.
|Lecture notes||Will appear on the lectures' web site.|
|Literature||1) Statistics: A guide to the use of statistical medhods in the Physical Sciences, R.J.Barlow; Wiley Verlag . |
2) J Statistical data analysis, G. Cowan, Oxford University Press; ISBN: 0198501552.
3) Statistische und numerische Methoden der Datenanalyse, V.Blobel und E.Lohrmann, Teubner Studienbuecher Verlag.
|Prerequisites / Notice||Grundkenntnisse in Kern- und Teilchenphysik vorausgesetzt.|
|701-0412-00L||Climate Systems||W||3 credits||2G||R. Knutti|
|Abstract||This course introduces the most important physical components of the climate system and their interactions. The mechanisms of anthropogenic climate change are analysed against the background of climate history and variability. Those completing the course will be in a position to identify and explain simple problems in the area of climate systems.|
|Objective||Students are able|
- to describe the most important physical components of the global climate system and sketch their interactions
- to explain the mechanisms of anthropogenic climate change
- to identify and explain simple problems in the area of climate systems
|Lecture notes||Copies of the slides are provided in electronic form.|
|Literature||A comprehensive list of references is provided in the class. Two books are|
- Hartmann, D., 1994: Global Physical Climatology. Academic Press, London, 411 pp.
- Peixoto, J.P. and A.H. Oort, 1992: Physics of Climate. American Institute of Physics, New York, 520 pp.
|Prerequisites / Notice||Teaching: Reto Knutti, several keynotes to special topics by other professors|
Course taught in german, slides in english
|701-1228-00L||Cloud Dynamics||W||4 credits||3G||U. Lohmann|
|Abstract||Hurricanes are among the most destructive elements in Atmospheric science. This lecture will discuss the requirements for their formation, longevity, damage potential and their relationship to global warming.|
|Objective||Understand how hurricane form and recognize that hurricane forecasts on short time scales and how they might change with global warming are very complex issues.|
|Content||In order to understand hurricane formation, their lifecycle, their potential damage and their connection with global warming, this course will review cloud formation, dynamics and microphysics relevant for understanding hurricane formation and lifecycle. This includes discussing differences to extratropical cyclones and mesoscale complex systems.|
|Lecture notes||Slides will be made available|
|Literature||Houze, R. A., Cloud Dynamics, Academic Press, 1993|
A literature list can be found here: http://www.iac.ethz.ch/edu/courses/master/modules/cloud_dynamics
|Prerequisites / Notice||At least one introductory lecture in Atmospheric Science or Instructor's consent.|
|» see also Fields of Specialization|