Search result: Catalogue data in Spring Semester 2023
Computational Science and Engineering Bachelor  | ||||||||||||||||||||||||||||||||||||||||||
 First Year Compulsory Courses | ||||||||||||||||||||||||||||||||||||||||||
| First Year Examination Block 1 Offered in the Autumn Semester | ||||||||||||||||||||||||||||||||||||||||||
| First Year Examination Block 2 | ||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||
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| 401-0232-10L | Analysis 2  | O | 8 credits | 4V + 2U | T. Rivière | |||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to differential calculus and integration in several variables. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | Einführung in die Grundlagen der Analysis | |||||||||||||||||||||||||||||||||||||||||
| Content | Differentiation in several variables, maxima and minima, the implicit function theorem, integration in several variables, integration over submanifolds, the theorems of Gauss and Stokes. | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Christian Blatter: Ingenieur-Analysis (Kapitel 4-6). Konrad Koenigsberger, Analysis II. | |||||||||||||||||||||||||||||||||||||||||
| 401-0302-10L | Complex Analysis | O | 4 credits | 3V + 1U | F. Da Lio | |||||||||||||||||||||||||||||||||||||
| Abstract | Basics of complex analysis in theory and applications, in particular the global properties of analytic functions. Introduction to the integral transforms and description of some applications | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | Erwerb von einigen grundlegenden Werkzeuge der komplexen Analysis. | |||||||||||||||||||||||||||||||||||||||||
| Content | Examples of analytic functions, Cauchy‘s theorem, Taylor and Laurent series, singularities of analytic functions, residues. Fourier series and Fourier integral, Laplace transform. | |||||||||||||||||||||||||||||||||||||||||
| Literature | J. Brown, R. Churchill: "Complex Analysis and Applications", McGraw-Hill 1995 T. Needham. Visual complex analysis. Clarendon Press, Oxford. 2004. M. Ablowitz, A. Fokas: "Complex variables: introduction and applications", Cambridge Text in Applied Mathematics, Cambridge University Press 1997 E. Kreyszig: "Advanced Engineering Analysis", Wiley 1999 J. Marsden, M. Hoffman: "Basic complex analysis", W. H. Freeman 1999 P. P. G. Dyke: "An Introduction to Laplace Transforms and Fourier Series", Springer 2004 A. Oppenheim, A. Willsky: "Signals & Systems", Prentice Hall 1997 M. Spiegel: "Laplace Transforms", Schaum's Outlines, Mc Graw Hill | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: Analysis I and II | |||||||||||||||||||||||||||||||||||||||||
| 402-0044-00L | Physics II | O | 4 credits | 3V + 1U | S. P. Quanz | |||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to the concepts and tools in physics with the help of demonstration experiments: electromagnetism, optics, introduction to modern physics. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | The concepts and tools in physics, as well as the methods of an experimental science are taught. The student should learn to identify, communicate and solve physical problems in his/her own field of science. | |||||||||||||||||||||||||||||||||||||||||
| Content | Electromagnetism (electric current, magnetic fields, electromagnetic induction, magnetic materials, Maxwell's equations) Optics (light, geometrical optics, interference and diffraction) Short introduction to quantum physics | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The lecture follows the book "Physik" by Paul A. Tipler. | |||||||||||||||||||||||||||||||||||||||||
| Literature | Paul A. Tipler and Gene Mosca Physik Springer Spektrum Verlag | |||||||||||||||||||||||||||||||||||||||||
| 529-4000-00L | Chemistry | O | 4 credits | 3G | E. C. Meister | |||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to chemistry with aspects of inorganic, organic and physical chemistry. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | - Understanding of simple models of chemical bonding and the three-dimensional molecular structure - Quantitative description of selected chemical systems by means of reaction equations and equilibria - Understanding of fundamental concepts of chemical kinetics (e.g. reaction order, rate law, rate constant) | |||||||||||||||||||||||||||||||||||||||||
| Content | Periodic system of the elements, chemical bonding (LCAO-MO), molecular structure (VSEPR), reactions, equilibria, chemical kinetics. | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Handouts of lecture presentations and additional supporting information will be offered. | |||||||||||||||||||||||||||||||||||||||||
| Literature | C.E. Housecroft, E.C. Constable, Chemistry. An Introduction to Organic, Inorganic and Physical Chemistry, 4th ed., Pearson: Harlow 2010. C.E. Mortimer, U. Müller, Chemie, 11. Auflage, Thieme: Stuttgart 2014. | |||||||||||||||||||||||||||||||||||||||||
| 252-0002-00L | Data Structures and Algorithms | O | 8 credits | 4V + 2U | M. Fischer, F. Friedrich Wicker | |||||||||||||||||||||||||||||||||||||
| Abstract | The course provides the foundations for the design and analysis of algorithms. Classic problems ranging from sorting up to problems on graphs are used to discuss common data structures, algorithms and algorithm design paradigms. The course also comprises an introduction to parallel and concurrent programming and the programming model of C++ is discussed in some depth. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | An understanding of the analysis and design of fundamental and common algorithms and data structures. Deeper insight into a modern programming model by means of the programming language C++. Knowledge regarding chances, problems and limits of parallel and concurrent programming. | |||||||||||||||||||||||||||||||||||||||||
| Content | Data structures and algorithms: mathematical tools for the analysis of algorithms (asymptotic function growth, recurrence equations, recurrence trees), informal proofs of algorithm correctness (invariants and code transformation), design paradigms for the development of algorithms (induction, divide-and-conquer, sweep-line method, backtracking and dynamic programming), classical algorithmic problems (searching, selection and sorting), data structures for different purposes (linked lists, hash tables, balanced search trees, quad trees, heaps, union-find), further tools for runtime analysis (e.g. amortized analysis). The relationship and tight coupling between algorithms and data structures is illustrated with geometric problems (convex hull, line intersections, closest point pairs) graph algorithms (traversals, topological sort, transitive closure, shortest paths, minimum spanning trees, max flow). Programming model of C++: correct and efficient memory handling, generic programming with templates, functional approaches with functors and lambda expressions. Parallel programming: concepts of parallel programming (Amdahl's and Gustavson's laws, task/data parallelism, scheduling), problems of concurrency (data races, bad interleavings, memory reordering), process synchronisation and communication in a shared memory system (mutual exclusion, semaphores, monitors, condition variables), progress conditions (freedom from deadlock, starvation). The concepts provided in the course are motivated and illustrated with practically relevant algorithms and applications. Exercises are carried out in Code-Expert, an online IDE and exercise management system. All required mathematical tools above high school level are covered, including a basic introduction to graph theory. | |||||||||||||||||||||||||||||||||||||||||
| Literature | (available from the course website) | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: Lecture Series 252-0835-00L Informatik I or equivalent knowledge in programming with C++. | |||||||||||||||||||||||||||||||||||||||||
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| Basic Courses | ||||||||||||||||||||||||||||||||||||||||||
| Block G1 All course units within Block G1 are offered in the autumn semester. | ||||||||||||||||||||||||||||||||||||||||||
| Block G2 All course units within Block G2 are offered in the autumn semester. | ||||||||||||||||||||||||||||||||||||||||||
| Block G3 | ||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||
| 401-0674-00L | Numerical Methods for Partial Differential Equations Not meant for BSc/MSc students of mathematics. | O | 10 credits | 2G + 2U + 2P + 4A | R. Hiptmair | |||||||||||||||||||||||||||||||||||||
| Abstract | Derivation, properties, and implementation of fundamental numerical methods for a few key partial differential equations: convection-diffusion, heat equation, wave equation, conservation laws. Implementation in C++ based on a finite element library. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | Main skills to be acquired in this course: * Ability to implement fundamental numerical methods for the solution of partial differential equations efficiently. * Ability to modify and adapt numerical algorithms guided by awareness of their mathematical foundations. * Ability to select and assess numerical methods in light of the predictions of theory * Ability to identify features of a PDE (= partial differential equation) based model that are relevant for the selection and performance of a numerical algorithm. * Ability to understand research publications on theoretical and practical aspects of numerical methods for partial differential equations. * Skills in the efficient implementation of finite element methods on unstructured meshes. This course is neither a course on the mathematical foundations and numerical analysis of methods nor an course that merely teaches recipes and how to apply software packages. | |||||||||||||||||||||||||||||||||||||||||
| Content | Second-order scalar elliptic boundary value problems Finite-element methods (FEM) FEM: Convergence and Accuracy Non-linear elliptic boundary value problems Second-order linear evolution problems Convection-diffusion problems Numerical methods for conservation laws | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The lecture will be taught in flipped classroom format: - Video tutorials for all thematic units will be published online. - Tablet notes accompanying the videos will be made available to the audience as PDF. - A comprehensive lecture document will cover all aspects of the course, see https://www.sam.math.ethz.ch/~grsam/NUMPDEFL/NUMPDE.pdf | |||||||||||||||||||||||||||||||||||||||||
| Literature | Chapters of the following books provide supplementary reading (detailed references in course material): * D. Braess: Finite Elemente, Theorie, schnelle Löser und Anwendungen in der Elastizitätstheorie, Springer 2007 (available online). * S. Brenner and R. Scott. Mathematical theory of finite element methods, Springer 2008 (available online). * A. Ern and J.-L. Guermond. Theory and Practice of Finite Elements, volume 159 of Applied Mathematical Sciences. Springer, New York, 2004. * Ch. Großmann and H.-G. Roos: Numerical Treatment of Partial Differential Equations, Springer 2007. * W. Hackbusch. Elliptic Differential Equations. Theory and Numerical Treatment, volume 18 of Springer Series in Computational Mathematics. Springer, Berlin, 1992. * P. Knabner and L. Angermann. Numerical Methods for Elliptic and Parabolic Partial Differential Equations, volume 44 of Texts in Applied Mathematics. Springer, Heidelberg, 2003. * S. Larsson and V. Thomée. Partial Differential Equations with Numerical Methods, volume 45 of Texts in Applied Mathematics. Springer, Heidelberg, 2003. * R. LeVeque. Finite Volume Methods for Hyperbolic Problems. Cambridge Texts in Applied Mathematics. Cambridge University Press, Cambridge, UK, 2002. However, study of supplementary literature is not important for for following the course. | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Mastery of basic calculus and linear algebra is taken for granted. Familiarity with fundamental numerical methods (solution methods for linear systems of equations, interpolation, approximation, numerical quadrature, numerical integration of ODEs) is essential. Important: Coding skills and experience in C++ are essential. Homework assignments involve substantial coding, partly based on a C++ finite element library. The written examination will be computer based and will comprise coding tasks. | |||||||||||||||||||||||||||||||||||||||||
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| 401-0604-00L | Probability Theory and Statistics | O | 4 credits | 2V + 1U | B. Acciaio | |||||||||||||||||||||||||||||||||||||
| Abstract | Probability models and applications, introduction to statistical estimation and statistical tests. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | Ability to understand the covered methods and models from probability theory and to apply them in other contexts. Ability to perform basic statistical tests and to interpret the results. | |||||||||||||||||||||||||||||||||||||||||
| Content | The concept of probability space and some classical models: the axioms of Kolmogorov, easy consequences, discrete models, densities, product spaces, relations between various models, distribution functions, transformations of probability distributions. Conditional probabilities, definition and examples, calculation of absolute probabilities from conditional probabilities, Bayes' formula, conditional distribution. Expectation of a random variable,application to coding, variance, covariance and correlation, linear estimator, law of large numbers, central limit theorem. Introduction to statistics: estimation of parameters and tests | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | yes | |||||||||||||||||||||||||||||||||||||||||
| Literature | Textbuch: P. Brémaud: 'An Introduction to Probabilistic Modeling', Springer, 1988. | |||||||||||||||||||||||||||||||||||||||||
| Block G4 | ||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||
| 529-0431-00L | Physical Chemistry III: Molecular Quantum Mechanics | O | 4 credits | 4G | F. Merkt, U. Hollenstein | |||||||||||||||||||||||||||||||||||||
| Abstract | Postulates of quantum mechanics, operator algebra, Schrödinger's equation, state functions and expectation values, matrix representation of operators, particle in a box, tunneling, harmonic oscillator, molecular vibrations, angular momentum and spin, generalised Pauli principle, perturbation theory, electronic structure of atoms and molecules, Born-Oppenheimer approximation. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | This is an introductory course in quantum mechanics. The course starts with an overview of the fundamental concepts of quantum mechanics and introduces the mathematical formalism. The postulates and theorems of quantum mechanics are discussed in the context of experimental and numerical determination of physical quantities. The course develops the tools necessary for the understanding and calculation of elementary quantum phenomena in atoms and molecules. | |||||||||||||||||||||||||||||||||||||||||
| Content | Postulates and theorems of quantum mechanics: operator algebra, Schrödinger's equation, state functions and expectation values. Linear motions: free particles, particle in a box, quantum mechanical tunneling, the harmonic oscillator and molecular vibrations. Angular momentum: electronic spin and orbital motion, molecular rotations. Electronic structure of atoms and molecules: the Pauli principle, angular momentum coupling, the Born-Oppenheimer approximation. Variational principle and perturbation theory. Discussion of bigger systems (solids, nano-structures). | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | A script written in German will be available. The script is, however, no replacement for personal notes during the lecture and does not cover all aspects discussed. | |||||||||||||||||||||||||||||||||||||||||
| 151-0102-00L | Fluid Dynamics I | O | 6 credits | 4V + 2U | F. Coletti | |||||||||||||||||||||||||||||||||||||
| Abstract | An introduction to the physical and mathematical foundations of fluid dynamics is given. Topics include dimensional analysis, integral and differential conservation laws, inviscid and viscous flows, Navier-Stokes equations, boundary layers, turbulent pipe flow. Elementary solutions and examples are presented. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | An introduction to the physical and mathematical principles of fluid dynamics. Fundamental terminology/principles and their application to simple problems. | |||||||||||||||||||||||||||||||||||||||||
| Content | Phenomena, applications, foundations dimensional analysis and similitude; kinematic description; conservation laws (mass, momentum, energy), integral and differential formulation; inviscid flows: Euler equations, stream filament theory, Bernoulli equation; viscous flows: Navier-Stokes equations; boundary layers; turbulence | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture notes (extended formulary) for the course are made available electronically. | |||||||||||||||||||||||||||||||||||||||||
| Literature | Recommended book: Fluid Mechanics, Kundu & Cohen & Dowling, 6th ed., Academic Press / Elsevier (2015). | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Voraussetzungen: Physik, Analysis | |||||||||||||||||||||||||||||||||||||||||
| 529-0483-00L | Statistical Physics and Computer Simulation | O | 6 credits | 2V + 1U | S. Riniker, P. H. Hünenberger | |||||||||||||||||||||||||||||||||||||
| Abstract | Principles and applications of statistical mechanics and equilibrium molecular dynamics, Monte Carlo simulation, stochastic dynamics and free energy calculation. Exercises using a MD simulation program to generate ensembles and subsequently calculate ensemble averages. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | Introduction to statistical mechanics with the aid of computer simulation; development of skills to carry out statistical mechanical calculations using computers and interpret the results. | |||||||||||||||||||||||||||||||||||||||||
| Content | Principles and applications of statistical mechanics and equilibrium molecular dynamics, Monte Carlo simulation, stochastic dynamics and free energy calculation. Exercises using a MD simulation program to generate ensembles and subsequently calculate ensemble averages. | |||||||||||||||||||||||||||||||||||||||||
| Literature | will be announced in the course | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Since the exercises on the computer do convey and test essentially different skills as those being conveyed during the lectures and tested at the written exam, the results of a small programming project will be presented in a 10-minutes talk by pairs of students who had been working on the project. Additional information will be provided in the first lecture. | |||||||||||||||||||||||||||||||||||||||||
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| Core Courses from Group I (Modules) | ||||||||||||||||||||||||||||||||||||||||||
| Module A | ||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||
| 151-0116-00L | High Performance Computing for Science and Engineering (HPCSE) for CSE | W | 7 credits | 4G + 2P | S. M. Martin, E. A. Economides | |||||||||||||||||||||||||||||||||||||
| Abstract | This course focuses on programming methods and tools for modern parallel systems, such as large-scale supercomputers with multi and many-core processors. Emphasis will be placed on techniques and models to maximize the performance of such systems. This is a hands-on course that relies on practical applications in science and engineering to demonstrate the importance of HPC. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | The objective of this course is to specialize students in the use of supercomputer systems and advanced (GPU) processors for solving large-scale scientific and engineering applications. Students will acquire tools that will enable them to solve computational problems, both in scientific research and engineering, that require large amounts of computation which can only be achieved by the efficient use of supercomputers and GPU processors. | |||||||||||||||||||||||||||||||||||||||||
| Content | The topics offered by this lecture include: - Large-scale computing topics: communication-tolerant programming and scalability. + Communication-Tolerant Programming + Hybrid Parallelism (MPI + OpenMP) + Work Tiling and Advanced Threading-Based Libraries - High-Throughput Computing and it's use in Monte-carlo and sampling methods for stochastic optimization methods and uncertainty quantification (UQ) - Principles and advance performance optimization topics for Many-Core (GPU) Programming | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | https://www.cse-lab.ethz.ch/teaching/hpcse-ii_fs23/ The materials include class notes, presentation slides, and lecture recordings. | |||||||||||||||||||||||||||||||||||||||||
| Literature | - Class notes - Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein - CUDA by example, J. Sanders and E. Kandrot | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Attendance of HPCSE I | |||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| Module B | ||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||
| 401-3670-00L | High-Performance Computing Lab for CSE | W | 7 credits | 4G + 1P | R. Käppeli, O. Schenk | |||||||||||||||||||||||||||||||||||||
| Abstract | This HPC Lab for CSE will focus on the effective exploitation of state-of-the-art HPC systems with a special focus on Computational Science and Engineering. The content of the course is tailored for 3th year Bachelor students interested in both learning parallel programming models, scientific mathematical libraries, and having hands-on experience using HPC systems. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | A goal of the course is that students will learn principles and practices of basic numerical methods and HPC to enable large-scale scientific simulations. This goal will be achieved within six to eight mini-projects with a focus on HPC and CSE. | |||||||||||||||||||||||||||||||||||||||||
| Content | Despite the success of parallel programming languages standardization, there is growing evidence that future computational science applications will depend on a computational software stack. The computational software approach in this HPC Lab is based on building and using small, simple software parts with flexible, easy-to-use interfaces. These simple software parts are toolkits - libraries containing basic services commonly needed by applications - and they build the underlying software layer for computational science and engineering applications. This course will introduce some of the many ways in which mathematical HPC software and numerical algorithms in computer science and mathematics play a role in computational science. The students will learn within several mini-projects how these algorithms and software can be used to enable large-scale scientific applications. It covers topics such as single core optimization for the memory hierarchy, parallel large-scale graph partititoning, parallel mathematical linear solvers, large-scale nonlinear optimization, and parallel software for the mathematical solution of nonlinear partial differential equations. The course takes both an algorithmic and a computing approach, focusing on techniques that have a high level of applicability to engineering, computer science, and industrial mathematics. | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Link to Moodle course: https://moodle-app2.let.ethz.ch/course/view.php?id=17005 | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Solid knowledge of the C programming language, parallel programming paradigms such as OpenMP and MPI, and numerical methods in scientific computing in the area of linear algebra, mathematical optimization, and partial differential equations. The students might continue to study these HPC techniques within the annual USI-CSCS Summer University on "Effective High-Performance Computing & Data Analytics". The content of the course is tailored for intermediate graduate students interested in both learning parallel programming models, and having hands-on experience using HPC systems. Starting from an introductory explanation of the available systems at CSCS, the course will progress to more applied topics such as parallel programming on accelerators, scientific libraries, and deep learning software frameworks. The following topics will be covered: GPU/ARM architectures, GPU/ARM programming, Message passing programming model (MPI), Performance optimization and scientific libraries, interactive supercomputing, Python libraries, Introduction to Machine Learning, and GPU/ARM optimized framework. This year’s USI-CSCS Summer University on HPC and Data Analytics, which will be composed of two sections – online from July 11 to 21, 2022, and on-site from July 23 to 25, 2022. The digital portion of this annual program will last two weeks (weekends excluded) and will be held from July 11 to 21, between 9:00 and 15:30 (/16:30 on the last day) CEST (Central European Summer Time). The optional in-person portion of the program is a three-day event from July 23 to 25 that we offer to students of the CSCS-USI Summer University as an additional option to connect with other students and actual research through encounters with Professors, to create collaborations and participate in engaging and interactive sessions. We look forward to welcoming and getting to know interested students selected for the summer university to the Italian-speaking area of Switzerland, and to sharing with them some entertaining moments around networking and inspiring lectures. Further information on this portion of the program will be provided in the following weeks. More information about the summer university is available here: Link | |||||||||||||||||||||||||||||||||||||||||
| Module C | ||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||
| 401-3670-00L | High-Performance Computing Lab for CSE | W | 7 credits | 4G + 1P | R. Käppeli, O. Schenk | |||||||||||||||||||||||||||||||||||||
| Abstract | This HPC Lab for CSE will focus on the effective exploitation of state-of-the-art HPC systems with a special focus on Computational Science and Engineering. The content of the course is tailored for 3th year Bachelor students interested in both learning parallel programming models, scientific mathematical libraries, and having hands-on experience using HPC systems. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | A goal of the course is that students will learn principles and practices of basic numerical methods and HPC to enable large-scale scientific simulations. This goal will be achieved within six to eight mini-projects with a focus on HPC and CSE. | |||||||||||||||||||||||||||||||||||||||||
| Content | Despite the success of parallel programming languages standardization, there is growing evidence that future computational science applications will depend on a computational software stack. The computational software approach in this HPC Lab is based on building and using small, simple software parts with flexible, easy-to-use interfaces. These simple software parts are toolkits - libraries containing basic services commonly needed by applications - and they build the underlying software layer for computational science and engineering applications. This course will introduce some of the many ways in which mathematical HPC software and numerical algorithms in computer science and mathematics play a role in computational science. The students will learn within several mini-projects how these algorithms and software can be used to enable large-scale scientific applications. It covers topics such as single core optimization for the memory hierarchy, parallel large-scale graph partititoning, parallel mathematical linear solvers, large-scale nonlinear optimization, and parallel software for the mathematical solution of nonlinear partial differential equations. The course takes both an algorithmic and a computing approach, focusing on techniques that have a high level of applicability to engineering, computer science, and industrial mathematics. | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Link to Moodle course: https://moodle-app2.let.ethz.ch/course/view.php?id=17005 | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Solid knowledge of the C programming language, parallel programming paradigms such as OpenMP and MPI, and numerical methods in scientific computing in the area of linear algebra, mathematical optimization, and partial differential equations. The students might continue to study these HPC techniques within the annual USI-CSCS Summer University on "Effective High-Performance Computing & Data Analytics". The content of the course is tailored for intermediate graduate students interested in both learning parallel programming models, and having hands-on experience using HPC systems. Starting from an introductory explanation of the available systems at CSCS, the course will progress to more applied topics such as parallel programming on accelerators, scientific libraries, and deep learning software frameworks. The following topics will be covered: GPU/ARM architectures, GPU/ARM programming, Message passing programming model (MPI), Performance optimization and scientific libraries, interactive supercomputing, Python libraries, Introduction to Machine Learning, and GPU/ARM optimized framework. This year’s USI-CSCS Summer University on HPC and Data Analytics, which will be composed of two sections – online from July 11 to 21, 2022, and on-site from July 23 to 25, 2022. The digital portion of this annual program will last two weeks (weekends excluded) and will be held from July 11 to 21, between 9:00 and 15:30 (/16:30 on the last day) CEST (Central European Summer Time). The optional in-person portion of the program is a three-day event from July 23 to 25 that we offer to students of the CSCS-USI Summer University as an additional option to connect with other students and actual research through encounters with Professors, to create collaborations and participate in engaging and interactive sessions. We look forward to welcoming and getting to know interested students selected for the summer university to the Italian-speaking area of Switzerland, and to sharing with them some entertaining moments around networking and inspiring lectures. Further information on this portion of the program will be provided in the following weeks. More information about the summer university is available here: Link | |||||||||||||||||||||||||||||||||||||||||
| Core Courses from Group II Recognition of 252-0220-00L Introduction to Machine Learning as a core course implies that this course unit cannot be recognised for the robotics field of specialisation. | ||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||
| 252-0232-00L | Software Engineering | W | 6 credits | 2V + 1U | F. Friedrich Wicker, M. Schwerhoff, H. Lehner | |||||||||||||||||||||||||||||||||||||
| Abstract | This course introduces both theoretical and applied aspects of software engineering. It covers: - Software Architecture - Informal and formal Modeling - Design Patterns - Software Engineering Principles - Code Refactoring - Program Testing | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | The course has two main objectives: - Obtain an end-to-end (both, theoretical and practical) understanding of the core techniques used for building quality software. - Be able to apply these techniques in practice. | |||||||||||||||||||||||||||||||||||||||||
| Content | While the lecture will provide the theoretical foundations for the various aspects of software engineering, the students will apply those techniques in project work that will span over the whole semester - involving all aspects of software engineering, from understanding requirements over design and implementation to deployment and change requests. | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | no lecture notes | |||||||||||||||||||||||||||||||||||||||||
| Literature | Will be announced in the lecture | |||||||||||||||||||||||||||||||||||||||||
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| 252-0220-00L | Introduction to Machine Learning Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact studiensekretariat@inf.ethz.ch | W | 8 credits | 4V + 2U + 1A | A. Krause, F. Yang | |||||||||||||||||||||||||||||||||||||
| Abstract | The course introduces the foundations of learning and making predictions based on data. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | The course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project. | |||||||||||||||||||||||||||||||||||||||||
| Content | - Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent) - Linear classification: Logistic regression (feature selection, sparsity, multi-class) - Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); k-nearest neighbor - Neural networks (backpropagation, regularization, convolutional neural networks) - Unsupervised learning (k-means, PCA, neural network autoencoders) - The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference) - Statistical decision theory (decision making based on statistical models and utility functions) - Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions) - Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE) - Bayesian approaches to unsupervised learning (Gaussian mixtures, EM) | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Designed to provide a basis for following courses: - Advanced Machine Learning - Deep Learning - Probabilistic Artificial Intelligence - Seminar "Advanced Topics in Machine Learning" | |||||||||||||||||||||||||||||||||||||||||
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| Astrophysics | ||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||
| 401-3961-00L | Physical Cosmology (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student. UZH Module Code: AST513 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html | W | 10 credits | 4V + 2U | University lecturers | |||||||||||||||||||||||||||||||||||||
| Abstract | We study the history of our universe on large scales. We first discuss key cosmological observations that led to our standard model of cosmology, and we study in detail the origin and the evolution of the Universe such as inflation, big bang nucleosynthesis, and cosmic microwave background anisotropies. In the second part we learn (relativistic) perturbation theory ... | |||||||||||||||||||||||||||||||||||||||||
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| Content | In this course (formerly known as theoretical cosmology), we study the history of our universe on large scales. We first discuss key cosmological observations that led to our standard model of cosmology, and we study in detail the origin and the evolution of the Universe such as inflation, big bang nucleosynthesis, and cosmic microwave background anisotropies. In the second part we learn (relativistic) perturbation theory and apply it to initial conditions, large-scale structure and weak gravitational lensing. | |||||||||||||||||||||||||||||||||||||||||
| Literature | Sugestted textbooks: H. Mo, F. Van den Bosch, S. White: Galaxy Formation and Evolution S. Carroll: Space-Time and Geometry: An Introduction to General Relativitv S. Dodelson: Modern Cosmoloay Secondary textbooks: S. Weinberg: Gravitation and Cosmology V. Mukhanov: Phvsical Foundations of Cosmology E. W. Kolb and M. S. Turner: The Early Universe N. Straumann: General relativity with applications to astrophysics A. Liddle and D. Lvth: Cosmological Inflation and Large Scale Structure | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Basic knowledge of general relativity is required. | |||||||||||||||||||||||||||||||||||||||||
| Physics of the Atmosphere | ||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||
| 701-1216-00L | Weather and Climate Models | W | 4 credits | 3G | C. Schär, D. Leutwyler, M. Wild | |||||||||||||||||||||||||||||||||||||
| Abstract | The course provides an introduction to weather and climate models. It discusses how these models are built addressing both the dynamical core and the physical parameterizations, and it provides an overview of how these models are used in numerical weather prediction and climate research. As a tutorial, students conduct a term project and build a simple atmospheric model using the language PYTHON. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | At the end of this course, students understand how weather and climate models are formulated from the governing physical principles, and how they are used for climate and weather prediction purposes. | |||||||||||||||||||||||||||||||||||||||||
| Content | The course provides an introduction into the following themes: numerical methods (finite differences and spectral methods); adiabatic formulation of atmospheric models (vertical coordinates, hydrostatic approximation); parameterization of physical processes (e.g. clouds, convection, boundary layer, radiation); atmospheric data assimilation and weather prediction; predictability (chaos-theory, ensemble methods); climate models (coupled atmospheric, oceanic and biogeochemical models); climate prediction. Hands-on experience with simple models will be acquired in the tutorials. | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Slides and lecture notes will be made available at Link | |||||||||||||||||||||||||||||||||||||||||
| Literature | List of literature will be provided. | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: to follow this course, you need some basic background in atmospheric science, numerical methods (e.g., "Numerische Methoden in der Umweltphysik", 701-0461-00L) as well as experience in programming. Previous experience with PYTHON is useful but not required. | |||||||||||||||||||||||||||||||||||||||||
| Chemistry | ||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||
| 529-0474-00L | Quantum Chemistry | W | 6 credits | 3G | M. Reiher, J. P. Unsleber, T. Weymuth | |||||||||||||||||||||||||||||||||||||
| Abstract | Introduction into the basic concepts of electronic structure theory and into numerical methods of quantum chemistry. Exercise classes are designed to deepen the theory; practical case studies using quantum chemical software to provide a 'hands-on' expertise in applying these methods. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | Nowadays, chemical research can be carried out in silico, an intellectual achievement for which Pople and Kohn have been awarded the Nobel prize of the year 1998. This lecture shows how that has been accomplished. It works out the many-particle theory of many-electron systems (atoms and molecules) and discusses its implementation into computer programs. A complete picture of quantum chemistry shall be provided that will allow students to carry out such calculations on molecules (for accompanying experimental work in the wet lab or as a basis for further study of the theory). | |||||||||||||||||||||||||||||||||||||||||
| Content | Basic concepts of many-particle quantum mechanics. Derivation of the many-electron theory for atoms and molecules; starting with the harmonic approximation for the nuclear problem and with Hartree-Fock theory for the electronic problem to Moeller-Plesset perturbation theory and configuration interaction and to coupled cluster and multi-configurational approaches. Density functional theory. Case studies using quantum mechanical software. | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Hand-outs in German will be provided for each lecture (they are supplemented by (computer) examples that continuously illustrate how the theory works). All information regarding this course, including links to the online streaming, will be available on this web page: https://reiher.ethz.ch/courses-and-seminars/exercises/QC_2023.html | |||||||||||||||||||||||||||||||||||||||||
| Literature | Textbooks on Quantum Chemistry: F.L. Pilar, Elementary Quantum Chemistry, Dover Publications I.N. Levine, Quantum Chemistry, Prentice Hall Hartree-Fock in basis set representation: A. Szabo and N. Ostlund, Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory, McGraw-Hill Textbooks on Computational Chemistry: F. Jensen, Introduction to Computational Chemistry, John Wiley & Sons C.J. Cramer, Essentials of Computational Chemistry, John Wiley & Sons | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Basic knowledge in quantum mechanics (e.g. through course physical chemistry III - quantum mechanics) required | |||||||||||||||||||||||||||||||||||||||||
| 227-0161-00L | Molecular and Materials Modelling | W | 6 credits | 2V + 2U | D. Passerone, C. Pignedoli | |||||||||||||||||||||||||||||||||||||
| Abstract | The course introduces the basic techniques to interpret experiments with contemporary atomistic simulation, including force fields or ab initio based molecular dynamics and Monte Carlo. Structural and electronic properties will be simulated hands-on for realistic systems. The modern methods of "big data" analysis applied to the screening of chemical structures will be introduced with examples. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | The ability to select a suitable atomistic approach to model a nanoscale system, and to employ a simulation package to compute quantities providing a theoretically sound explanation of a given experiment. This includes knowledge of empirical force fields and insight in electronic structure theory, in particular density functional theory (DFT). Understanding the advantages of Monte Carlo and molecular dynamics (MD), and how these simulation methods can be used to compute various static and dynamic material properties. Basic understanding on how to simulate different spectroscopies (IR, X-ray, UV/VIS). Performing a basic computational experiment: interpreting the experimental input, choosing theory level and model approximations, performing the calculations, collecting and representing the results, discussing the comparison to the experiment. | |||||||||||||||||||||||||||||||||||||||||
| Content | -Classical force fields in molecular and condensed phase systems -Methods for finding stationary states in a potential energy surface -Monte Carlo techniques applied to nanoscience -Classical molecular dynamics: extracting quantities and relating to experimentally accessible properties -From molecular orbital theory to quantum chemistry: chemical reactions -Condensed phase systems: from periodicity to band structure -Larger scale systems and their electronic properties: density functional theory and its approximations -Advanced molecular dynamics: Correlation functions and extracting free energies -The use of Smooth Overlap of Atomic Positions (SOAP) descriptors in the evaluation of the (dis)similarity of crystalline, disordered and molecular compounds | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | A script will be made available and complemented by literature references. | |||||||||||||||||||||||||||||||||||||||||
| Literature | D. Frenkel and B. Smit, Understanding Molecular Simulations, Academic Press, 2002. M. P. Allen and D.J. Tildesley, Computer Simulations of Liquids, Oxford University Press 1990. C. J. Cramer, Essentials of Computational Chemistry. Theories and Models, Wiley 2004 G. L. Miessler, P. J. Fischer, and Donald A. Tarr, Inorganic Chemistry, Pearson 2014. K. Huang, Statistical Mechanics, Wiley, 1987. N. W. Ashcroft, N. D. Mermin, Solid State Physics, Saunders College 1976. E. Kaxiras, Atomic and Electronic Structure of Solids, Cambridge University Press 2010. | |||||||||||||||||||||||||||||||||||||||||
| Fluid Dynamics | ||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||
| 151-0208-00L | Computational Methods for Flow, Heat and Mass Transfer Problems | W | 4 credits | 4G | D. W. Meyer-Massetti | |||||||||||||||||||||||||||||||||||||
| Abstract | Numerical methods for the solution of flow, heat & mass transfer problems are presented and illustrated by analytical & computer exercises. The course is taught using the flipped classroom format. | |||||||||||||||||||||||||||||||||||||||||
| Learning objective | Knowledge of and practical experience with discretization and solution methods for computational fluid dynamics and heat and mass transfer problems | |||||||||||||||||||||||||||||||||||||||||
| Content | - Introduction with application examples, steps to a numerical solution - Classification of PDEs, application examples - Finite differences - Finite volumes - Method of weighted residuals, spectral methods, finite elements - Boundary integral method - Stability analysis, consistency, convergence - Numerical solution methods, linear solvers The learning materials are illustrated with practical examples. | |||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Slides and lecture notes will be handed out. | |||||||||||||||||||||||||||||||||||||||||
| Literature | References are provided during the lecture. | |||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Basic knowledge in fluid dynamics, thermodynamics and programming (lecture: "Models, Algorithms and Data: Introduction to Computing") | |||||||||||||||||||||||||||||||||||||||||
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