Search result: Catalogue data in Spring Semester 2021
Computational Science and Engineering Master | ||||||
Electives In the ‘electives’ subcategory, at least two course units must be successfully completed. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
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151-3202-00L | Product Development and Engineering Design Number of participants limited to 60. | W | 4 credits | 2G | K. Shea, T. Stankovic | |
Abstract | The course introduces students to the product development process. In a team, you will explore the early phases of conceptual development and product design, from ideation and concept generation through to hands-on prototyping. This is an opportunity to gain product development experience and improve your skills in prototyping and presenting your product ideas. The project topic changes each year. | |||||
Objective | The course introduces you to the product development process and methods in engineering design for: product planning, user-centered design, creating product specifications, ideation including concept generation and selection methods, material selection methods and prototyping. Further topics include design for manufacture and design for additive manufacture. You will actively apply the process and methods learned throughout the semester in a team on a product development project including prototyping. | |||||
Content | Weekly topics accompanying the product development project include: 1 Introduction to Product Development and Engineering Design 2 Product Planning and Social-Economic-Technology (SET) Factors 3 User-Centered Design and Product Specifications 4 Concept Generation and Selection Methods 5 System Design and Embodiment Design 6 Prototyping and Prototype Planning 7 Material Selection in Engineering Design 8 Design for Manufacture and Design for Additive Manufacture | |||||
Lecture notes | available on Moodle | |||||
Literature | Ulrich, Eppinger, and Yang, Product Design and Development. 7th ed., McGraw-Hill Education, 2020. Cagan and Vogel, Creating Breakthrough Products: Revealing the Secrets that Drive Global Innovation, 2nd Edition, Pearson Education, 2013. | |||||
Prerequisites / Notice | Although the course is offered to ME (BSc and MSc) and CS (BSc and MSc) students, priority will be given to ME BSc students in the Focus Design, Mechanics, and Materials if the course is full. | |||||
151-0840-00L | Optimization and Machine Learning Note: previous course title until FS20 "Principles of FEM-Based Optimization and Robustness Analysis". | W | 4 credits | 2V + 2U | B. Berisha, D. Mohr | |
Abstract | The course teaches the basics of nonlinear optimization and concepts of machine learning. An introduction to the finite element method allows an extension of the application area to real engineering problems such as structural optimization and modeling of material behavior on different length scales. | |||||
Objective | Students will learn mathematical optimization methods including gradient based and gradient free methods as well as established algorithms in the context of machine learning to solve real engineering problems, which are generally non-linear in nature. Strategies to ensure efficient training of machine learning models based on large data sets define another teaching goal of the course. Optimization tools (MATLAB, LS-Opt, Python) and the finite element program ABAQUS are presented to solve both general and real engineering problems. | |||||
Content | - Introduction into Nonlinear Optimization - Design of Experiments DoE - Introduction into Nonlinear Finite Element Analysis - Optimization based on Meta Modeling Techniques - Shape and Topology Optimization - Robustness and Sensitivity Analysis - Fundamentals of Machine Learning - Generalized methods for regression and classification, Neural Networks, Support Vector machines - Supervised and unsupervised learning | |||||
Lecture notes | Lecture slides and literature | |||||
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; concentrated solar power; solar photovoltaics. Fuel cells: characteristics, fuel reforming and combined cycles. | |||||
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. • Students are able to evaluate and select the most appropriate VR technology for a given task regarding: o Visualization technologies displays/projection systems/head-mounted displays o Tracking systems (inertia/optical/electromagnetic) o Interaction technologies (sensing gloves/real walking/eye tracking/touch/etc.) • Students are able to develop a VR application • Students are able to apply VR to industrial needs • Students will be able to apply the gained knowledge to a practical realization • Students will be able to compare different operation principles (VR/AR/MR/XR) | |||||
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: keine 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 | Objectives, Concepts and Methods of Digitalization, Digital Product and Product Lifecycle Management (PLM), Industry 4.0 Concepts for Digitalization: Product Structures, Optimization of Engineering Processes with digital models in Sales, Production, Service, Digital Twin versus Digital Thread PLM Fundamentals: Objects, Structures, Processes, Integrations, Visualization Best Practices | |||||
Objective | Students learn the fundamentals and concepts of Digitalization along the in the product lifecycle on the foundation of Product Lifecycle Management (PLM) technologies, the usage of databases, the integration of CAx systems and Visualization/AR, the configuration of computer-based collaboration leveraging IT-standards as well as variant and configuration management to enable an efficient utilization of the digital product approach in industry 4.0. | |||||
Content | Possibilities and potential of modern IT applications focussing on PLM and CAx technologies for targeted utilization in the context of product platform - business processes - IT tools. Introduction to the concepts of Product Lifecycle Management (PLM): information modeling, data management, revision, usage and distribution of product data. Structure and functional principles of PLM systems. Integration of new IT technologies in business processes. Possibilities of publication and automatic configuration of product variants via the Internet. Using state-of-the-art information and communication technologies to develop products globally across distributed locations. Interfaces in computer-integrated product development. Selection, configuration, adaptation and introduction of PLM systems. Examples and case studies for industrial usage of modern information technologies. Learning modules: - Introduction to Digitalization (Digital Product, PLM technology) - Database technology (foundation of digitalization) - Object Management - Object Classification - Object identification with Part Numbering Systems - CAx/PLM integration with Visualization/AR - Workflow & Change Management - Interfaces of the Digital Product - Enterprise Application Integration (EAI) | |||||
Lecture notes | Didactic concept / learning materials: The course consists of lectures and exercises based on practical examples. Provision of lecture handouts and script digitally in Moodle. | |||||
Prerequisites / Notice | Prerequisites: None Recommended: Fokus-Project, interest in Digitalization Lecture appropriate for D-MAVT, D-MTEC, D-ITET and D-INFK Testat/Credit Requirements / Exam: - execution of exercises in teams (recommended) - Oral exam 30 minutes, based on concrete problem cases | |||||
151-0660-00L | Model Predictive Control | W | 4 credits | 2V + 1U | M. Zeilinger, A. Carron | |
Abstract | Model predictive control is a flexible paradigm that defines the control law as an optimization problem, enabling the specification of time-domain objectives, high performance control of complex multivariable systems and the ability to explicitly enforce constraints on system behavior. This course provides an introduction to the theory and practice of MPC and covers advanced topics. | |||||
Objective | Design and implement Model Predictive Controllers (MPC) for various system classes to provide high performance controllers with desired properties (stability, tracking, robustness,..) for constrained systems. | |||||
Content | - Review of required optimal control theory - Basics on optimization - Receding-horizon control (MPC) for constrained linear systems - Theoretical properties of MPC: Constraint satisfaction and stability - Computation: Explicit and online MPC - Practical issues: Tracking and offset-free control of constrained systems, soft constraints - Robust MPC: Robust constraint satisfaction - Nonlinear MPC: Theory and computation - Hybrid MPC: Modeling hybrid systems and logic, mixed-integer optimization - Simulation-based project providing practical experience with MPC | |||||
Lecture notes | Script / lecture notes will be provided. | |||||
Prerequisites / Notice | One semester course on automatic control, Matlab, linear algebra. Courses on signals and systems and system modeling are recommended. Important concepts to start the course: State-space modeling, basic concepts of stability, linear quadratic regulation / unconstrained optimal control. Expected student activities: Participation in lectures, exercises and course project; homework (~2hrs/week). | |||||
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) | |||||
151-0980-00L | 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 in 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-structure interaction) and use them to model physiological flow processes. The list of studied topics includes the cardiovascular system and related diseases, blood rheology, microcirculation, respiratory fluid dynamics and fluid dynamics of the inner ear. | |||||
Lecture notes | Lecture notes are provided electronically. | |||||
Literature | A list of books on selected topics of biofluiddynamics can be found on the course web page. | |||||
151-0530-00L | Nonlinear Dynamics and Chaos II | W | 4 credits | 4G | G. Haller | |
Abstract | The internal structure of chaos; Hamiltonian dynamical systems; Normally hyperbolic invariant manifolds; Geometric singular perturbation theory; Finite-time dynamical systems | |||||
Objective | The course introduces the student to advanced, comtemporary concepts of nonlinear dynamical systems analysis. | |||||
Content | I. The internal structure of chaos: symbolic dynamics, Bernoulli shift map, sub-shifts of finite type; chaos is numerical iterations. II.Hamiltonian dynamical systems: conservation and recurrence, stability of fixed points, integrable systems, invariant tori, Liouville-Arnold-Jost Theorem, KAM theory. III. Normally hyperbolic invariant manifolds: Crash course on differentiable manifolds, existence, persistence, and smoothness, applications. IV. Geometric singular perturbation theory: slow manifolds and their stability, physical examples. V. Finite-time dynamical system; detecting Invariant manifolds and coherent structures in finite-time flows | |||||
Lecture notes | Students have to prepare their own lecture notes | |||||
Literature | Books will be recommended in class | |||||
Prerequisites / Notice | Nonlinear Dynamics I (151-0532-00) or equivalent | |||||
101-0178-01L | Uncertainty Quantification in Engineering | W | 3 credits | 2G | S. Marelli, B. Sudret | |
Abstract | Uncertainty quantification aims at studying the impact of aleatory and epistemic uncertainty onto computational models used in science and engineering. The course introduces the basic concepts of uncertainty quantification: probabilistic modelling of data (copula theory), uncertainty propagation techniques (Monte Carlo simulation, polynomial chaos expansions), and sensitivity analysis. | |||||
Objective | After this course students will be able to properly pose an uncertainty quantification problem, select the appropriate computational methods and interpret the results in meaningful statements for field scientists, engineers and decision makers. The course is suitable for any master/Ph.D. student in engineering or natural sciences, physics, mathematics, computer science with a basic knowledge in probability theory. | |||||
Content | The course introduces uncertainty quantification through a set of practical case studies that come from civil, mechanical, nuclear and electrical engineering, from which a general framework is introduced. The course in then divided into three blocks: probabilistic modelling (introduction to copula theory), uncertainty propagation (Monte Carlo simulation and polynomial chaos expansions) and sensitivity analysis (correlation measures, Sobol' indices). Each block contains lectures and tutorials using Matlab and the in-house software UQLab (Link). | |||||
Lecture notes | Detailed slides are provided for each lecture. A printed script gathering all the lecture slides may be bought at the beginning of the semester. | |||||
Prerequisites / Notice | A basic background in probability theory and statistics (bachelor level) is required. A summary of useful notions will be handed out at the beginning of the course. A good knowledge of Matlab is required to participate in the tutorials and for the mini-project. | |||||
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 includes a self-contained introduction of the pertinent basics of "abstract" algebra. | |||||
Objective | The course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course includes a self-contained introduction of the pertinent basics of "abstract" algebra. | |||||
Content | Error correcting codes: 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, polar codes, Reed-Solomon codes. Algebra: groups, rings, homomorphisms, quotient groups, ideals, finite fields, vector spaces, polynomials. | |||||
Lecture notes | Lecture Notes (english) | |||||
227-0420-00L | Information Theory II | W | 6 credits | 4G | A. Lapidoth, S. M. Moser | |
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's objective is to introduce the students to additional information measures and to equip them with the tools that are needed to conduct research in Information Theory as it relates to Communication Networks and to Statistics. | |||||
Content | Sanov's Theorem, Rényi entropy and guessing, differential entropy, maximum entropy, the Gaussian channel, the entropy-power inequality, the broadcast channel, the multiple-access channel, Slepian-Wolf coding, the Gelfand-Pinsker problem, and Fisher information. | |||||
Lecture notes | n/a | |||||
Literature | T.M. Cover and J.A. Thomas, Elements of Information Theory, second edition, Wiley 2006 | |||||
Prerequisites / Notice | Basic introductory course on Information Theory. | |||||
227-0434-10L | Mathematics of Information | W | 8 credits | 3V + 2U + 2A | H. Bölcskei | |
Abstract | The class focuses on mathematical aspects of 1. Information science: Sampling theorems, frame theory, compressed sensing, sparsity, super-resolution, spectrum-blind sampling, subspace algorithms, dimensionality reduction 2. Learning theory: Approximation theory, greedy algorithms, uniform laws of large numbers, Rademacher complexity, Vapnik-Chervonenkis dimension | |||||
Objective | The aim of the class is to familiarize the students with the most commonly used mathematical theories in data science, high-dimensional data analysis, and learning theory. The class consists of the lecture, exercise sessions with homework problems, and of a research project, which can be carried out either individually or in groups. The research project consists of either 1. software development for the solution of a practical signal processing or machine learning problem or 2. the analysis of a research paper or 3. a theoretical research problem of suitable complexity. Students are welcome to propose their own project at the beginning of the semester. The outcomes of all projects have to be presented to the entire class at the end of the semester. | |||||
Content | Mathematics of Information 1. Signal representations: Frame theory, wavelets, Gabor expansions, sampling theorems, density theorems 2. Sparsity and compressed sensing: Sparse linear models, uncertainty relations in sparse signal recovery, super-resolution, spectrum-blind sampling, subspace algorithms (ESPRIT), estimation in the high-dimensional noisy case, Lasso 3. Dimensionality reduction: Random projections, the Johnson-Lindenstrauss Lemma Mathematics of Learning 4. Approximation theory: Nonlinear approximation theory, best M-term approximation, greedy algorithms, fundamental limits on compressibility of signal classes, Kolmogorov-Tikhomirov epsilon-entropy of signal classes, optimal compression of signal classes 5. Uniform laws of large numbers: Rademacher complexity, Vapnik-Chervonenkis dimension, classes with polynomial discrimination | |||||
Lecture notes | Detailed lecture notes will be provided at the beginning of the semester. | |||||
Prerequisites / Notice | This course is aimed at students with a background in basic linear algebra, analysis, statistics, and probability. We encourage students who are interested in mathematical data science to take both this course and "401-4944-20L Mathematics of Data Science" by Prof. A. Bandeira. The two courses are designed to be complementary. H. Bölcskei and A. Bandeira | |||||
227-0104-00L | Communication and Detection Theory | W | 6 credits | 4G | A. Lapidoth | |
Abstract | This course teaches the foundations of modern digital communications and detection theory. 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; hypothesis testing; Gaussian stochastic processes; and detection in white Gaussian noise. | |||||
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 | - Baseband representation of passband signals. - Bandwidth and inner products in baseband and passband. - The geometry of the space of energy-limited signals. - The Sampling Theorem as an orthonormal expansion. - Sampling passband signals. - Pulse Amplitude Modulation (PAM): energy, power, and power spectral density. - Nyquist Pulses. - Quadrature Amplitude Modulation (QAM). - Hypothesis testing. - The Bhattacharyya Bound. - The multivariate Gaussian distribution - Gaussian stochastic processes. - Detection in white Gaussian noise. | |||||
Lecture notes | n/a | |||||
Literature | A. Lapidoth, A Foundation in Digital Communication, Cambridge University Press, 2nd edition (2017) | |||||
227-0120-00L | Communication Networks | W | 6 credits | 4G | L. Vanbever | |
Abstract | At the end of this course, you will understand the fundamental concepts behind communication networks and the Internet. Specifically, you will be able to: - understand how the Internet works; - build and operate Internet-like infrastructures; - identify the right set of metrics to evaluate the performance of a network and propose ways to improve it. | |||||
Objective | At the end of the course, the students will understand the fundamental concepts of communication networks and Internet-based communications. Specifically, students will be able to: - understand how the Internet works; - build and operate Internet-like network infrastructures; - identify the right set of metrics to evaluate the performance or the adequacy of a network and propose ways to improve it (if any). The course will introduce the relevant mechanisms used in today's networks both from an abstract perspective but also from a practical one by presenting many real-world examples and through multiple hands-on projects. For more information about the lecture, please visit: Link | |||||
Lecture notes | Lecture notes and material for the course will be available before each course on: Link | |||||
Literature | Most of course follows the textbook "Computer Networking: A Top-Down Approach (6th Edition)" by Kurose and Ross. | |||||
Prerequisites / Notice | No prior networking background is needed. The course will include some programming assignments (in Python) for which the material covered in Technische Informatik 1 (227-0013-00L) will be useful. | |||||
227-0159-00L | Semiconductor Devices: Quantum Transport at the Nanoscale | W | 6 credits | 2V + 2U | M. Luisier, A. Emboras | |
Abstract | This class offers an introduction into quantum transport theory, a rigorous approach to electron transport at the nanoscale. It covers different topics such as bandstructure, Wave Function and Non-equilibrium Green's Function formalisms, and electron interactions with their environment. Matlab exercises accompany the lectures where students learn how to develop their own transport simulator. | |||||
Objective | The continuous scaling of electronic devices has given rise to structures whose dimensions do not exceed a few atomic layers. At this size, electrons do not behave as particle any more, but as propagating waves and the classical representation of electron transport as the sum of drift-diffusion processes fails. The purpose of this class is to explore and understand the displacement of electrons through nanoscale device structures based on state-of-the-art quantum transport methods and to get familiar with the underlying equations by developing his own nanoelectronic device simulator. | |||||
Content | The following topics will be addressed: - Introduction to quantum transport modeling - Bandstructure representation and effective mass approximation - Open vs closed boundary conditions to the Schrödinger equation - Comparison of the Wave Function and Non-equilibrium Green's Function formalisms as solution to the Schrödinger equation - Self-consistent Schödinger-Poisson simulations - Quantum transport simulations of resonant tunneling diodes and quantum well nano-transistors - Top-of-the-barrier simulation approach to nano-transistor - Electron interactions with their environment (phonon, roughness, impurity,...) - Multi-band transport models | |||||
Lecture notes | Lecture slides are distributed every week and can be found at Link | |||||
Literature | Recommended textbook: "Electronic Transport in Mesoscopic Systems", Supriyo Datta, Cambridge Studies in Semiconductor Physics and Microelectronic Engineering, 1997 | |||||
Prerequisites / Notice | Basic knowledge of semiconductor device physics and quantum mechanics | |||||
227-0558-00L | Principles of Distributed Computing | W | 7 credits | 2V + 2U + 2A | R. Wattenhofer, M. Ghaffari | |
Abstract | We study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques. | |||||
Objective | Distributed computing is essential in modern computing and communications systems. Examples are on the one hand large-scale networks such as the Internet, and on the other hand multiprocessors such as your new multi-core laptop. This course introduces the principles of distributed computing, emphasizing the fundamental issues underlying the design of distributed systems and networks: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques, basically the "pearls" of distributed computing. We will cover a fresh topic every week. | |||||
Content | Distributed computing models and paradigms, e.g. message passing, shared memory, synchronous vs. asynchronous systems, time and message complexity, peer-to-peer systems, small-world networks, social networks, sorting networks, wireless communication, and self-organizing systems. Distributed algorithms, e.g. leader election, coloring, covering, packing, decomposition, spanning trees, mutual exclusion, store and collect, arrow, ivy, synchronizers, diameter, all-pairs-shortest-path, wake-up, and lower bounds | |||||
Lecture notes | Available. Our course script is used at dozens of other universities around the world. | |||||
Literature | Lecture Notes By Roger Wattenhofer. These lecture notes are taught at about a dozen different universities through the world. Distributed Computing: Fundamentals, Simulations and Advanced Topics Hagit Attiya, Jennifer Welch. McGraw-Hill Publishing, 1998, ISBN 0-07-709352 6 Introduction to Algorithms Thomas Cormen, Charles Leiserson, Ronald Rivest. The MIT Press, 1998, ISBN 0-262-53091-0 oder 0-262-03141-8 Disseminatin of Information in Communication Networks Juraj Hromkovic, Ralf Klasing, Andrzej Pelc, Peter Ruzicka, Walter Unger. Springer-Verlag, Berlin Heidelberg, 2005, ISBN 3-540-00846-2 Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes Frank Thomson Leighton. Morgan Kaufmann Publishers Inc., San Francisco, CA, 1991, ISBN 1-55860-117-1 Distributed Computing: A Locality-Sensitive Approach David Peleg. Society for Industrial and Applied Mathematics (SIAM), 2000, ISBN 0-89871-464-8 | |||||
Prerequisites / Notice | Course pre-requisites: Interest in algorithmic problems. (No particular course needed.) | |||||
252-0211-00L | Information Security | W | 8 credits | 4V + 3U | D. Basin, S. Capkun | |
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 | |||||
263-4660-00L | Applied Cryptography Number of participants limited to 150. | W | 8 credits | 3V + 2U + 2P | K. Paterson | |
Abstract | This course will introduce the basic primitives of cryptography, using rigorous syntax and game-based security definitions. The course will show how these primitives can be combined to build cryptographic protocols and systems. | |||||
Objective | The goal of the course is to put students' understanding of cryptography on sound foundations, to enable them to start to build well-designed cryptographic systems, and to expose them to some of the pitfalls that arise when doing so. | |||||
Content | Basic symmetric primitives (block ciphers, modes, hash functions); generic composition; AEAD; basic secure channels; basic public key primitives (encryption,signature, DH key exchange); ECC; randomness; applications. | |||||
Literature | Textbook: Boneh and Shoup, “A Graduate Course in Applied Cryptography”, Link. | |||||
Prerequisites / Notice | Students should have taken the D-INFK Bachelor's course “Information Security" (252-0211-00) or an alternative first course covering cryptography at a similar level. / In this course, we will use Moodle for content delivery: Link. | |||||
401-4656-21L | Deep Learning in Scientific Computing Aimed at students in a Master's Programme in Mathematics, Engineering and Physics. | W | 6 credits | 2V + 1U | S. Mishra | |
Abstract | Machine Learning, particularly deep learning is being increasingly applied to perform, enhance and accelerate computer simulations of models in science and engineering. This course aims to present a highly topical selection of themes in the general area of deep learning in scientific computing, with an emphasis on the application of deep learning algorithms for systems, modeled by PDEs. | |||||
Objective | The objective of this course will be to introduce students to advanced applications of deep learning in scientific computing. The focus will be on the design and implementation of algorithms as well as on the underlying theory that guarantees reliability of the algorithms. We will provide several examples of applications in science and engineering where deep learning based algorithms outperform state of the art methods. | |||||
Content | A selection of the following topics will be presented in the lectures. 1. Issues with traditional methods for scientific computing such as Finite Element, Finite Volume etc, particularly for PDE models with high-dimensional state and parameter spaces. 2. Introduction to Deep Learning: Artificial Neural networks, Supervised learning, Stochastic gradient descent algorithms for training, different architectures: Convolutional Neural Networks, Recurrent Neural Networks, ResNets. 3. Theoretical Foundations: Universal approximation properties of the Neural networks, Bias-Variance decomposition, Bounds on approximation and generalization errors. 4. Supervised deep learning for solutions fields and observables of high-dimensional parametric PDEs. Use of low-discrepancy sequences and multi-level training to reduce generalization error. 5. Uncertainty Quantification for PDEs with supervised learning algorithms. 6. Deep Neural Networks as Reduced order models and prediction of solution fields. 7. Active Learning algorithms for PDE constrained optimization. 8. Recurrent Neural Networks and prediction of time series for dynamical systems. 9. Physics Informed Neural networks (PINNs) for the forward problem for PDEs. Applications to high-dimensional PDEs. 10. PINNs for inverse problems for PDEs, parameter identification, optimal control and data assimilation. All the algorithms will be illustrated on a variety of PDEs: diffusion models, Black-Scholes type PDEs from finance, wave equations, Euler and Navier-Stokes equations, hyperbolic systems of conservation laws, Dispersive PDEs among others. | |||||
Lecture notes | Lecture notes will be provided at the end of the course. | |||||
Literature | All the material in the course is based on research articles written in last 1-2 years. The relevant references will be provided. | |||||
Prerequisites / Notice | The students should be familiar with numerical methods for PDEs, for instance in courses such as Numerical Methods for PDEs for CSE, Numerical analysis of Elliptic and Parabolic PDEs, Numerical methods for hyperbolic PDEs, Computational methods for Engineering Applications. Some familiarity with basic concepts in machine learning will be beneficial. The exercises in the course rely on standard machine learning frameworks such as KERAS, TENSORFLOW or PYTORCH. So, competence in Python is helpful. |
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