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  

401023210L  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: IngenieurAnalysis (Kapitel 46). Konrad Koenigsberger, Analysis II.  
401030210L  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", McGrawHill 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  
402004400L  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  
529400000L  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 threedimensional 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 (LCAOMO), 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.  
252000200L  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, divideandconquer, sweepline 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, unionfind), 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 CodeExpert, 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 252083500L Informatik I or equivalent knowledge in programming with C++.  
Competencies 
 
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  
401067400L  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: convectiondiffusion, 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  Secondorder scalar elliptic boundary value problems Finiteelement methods (FEM) FEM: Convergence and Accuracy Nonlinear elliptic boundary value problems Secondorder linear evolution problems Convectiondiffusion 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.  
Competencies 
 
401060400L  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  
529043100L  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, BornOppenheimer 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 BornOppenheimer approximation. Variational principle and perturbation theory. Discussion of bigger systems (solids, nanostructures).  
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.  
151010200L  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, NavierStokes 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: NavierStokes 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  
529048300L  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 10minutes talk by pairs of students who had been working on the project. Additional information will be provided in the first lecture.  
Competencies 
 
Core Courses from Group I (Modules)  
Module A  
Number  Title  Type  ECTS  Hours  Lecturers  
151011600L  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 largescale supercomputers with multi and manycore processors. Emphasis will be placed on techniques and models to maximize the performance of such systems. This is a handson 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 largescale 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:  Largescale computing topics: communicationtolerant programming and scalability. + CommunicationTolerant Programming + Hybrid Parallelism (MPI + OpenMP) + Work Tiling and Advanced ThreadingBased Libraries  HighThroughput Computing and it's use in Montecarlo and sampling methods for stochastic optimization methods and uncertainty quantification (UQ)  Principles and advance performance optimization topics for ManyCore (GPU) Programming  
Lecture notes  https://www.cselab.ethz.ch/teaching/hpcseii_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 
 
Module B  
Number  Title  Type  ECTS  Hours  Lecturers  
401367000L  HighPerformance 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 stateoftheart 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 handson 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 largescale scientific simulations. This goal will be achieved within six to eight miniprojects 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, easytouse 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 miniprojects how these algorithms and software can be used to enable largescale scientific applications. It covers topics such as single core optimization for the memory hierarchy, parallel largescale graph partititoning, parallel mathematical linear solvers, largescale 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://moodleapp2.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 USICSCS Summer University on "Effective HighPerformance Computing & Data Analytics". The content of the course is tailored for intermediate graduate students interested in both learning parallel programming models, and having handson 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 USICSCS Summer University on HPC and Data Analytics, which will be composed of two sections – online from July 11 to 21, 2022, and onsite 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 inperson portion of the program is a threeday event from July 23 to 25 that we offer to students of the CSCSUSI 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 Italianspeaking 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  
401367000L  HighPerformance 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 stateoftheart 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 handson 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 largescale scientific simulations. This goal will be achieved within six to eight miniprojects 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, easytouse 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 miniprojects how these algorithms and software can be used to enable largescale scientific applications. It covers topics such as single core optimization for the memory hierarchy, parallel largescale graph partititoning, parallel mathematical linear solvers, largescale 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://moodleapp2.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 USICSCS Summer University on "Effective HighPerformance Computing & Data Analytics". The content of the course is tailored for intermediate graduate students interested in both learning parallel programming models, and having handson 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 USICSCS Summer University on HPC and Data Analytics, which will be composed of two sections – online from July 11 to 21, 2022, and onsite 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 inperson portion of the program is a threeday event from July 23 to 25 that we offer to students of the CSCSUSI 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 Italianspeaking 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 252022000L 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  
252023200L  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 endtoend (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  
Competencies 
 
252022000L  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 handson experience in a course project.  
Content   Linear regression (overfitting, crossvalidation/bootstrap, model selection, regularization, [stochastic] gradient descent)  Linear classification: Logistic regression (feature selection, sparsity, multiclass)  Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); knearest neighbor  Neural networks (backpropagation, regularization, convolutional neural networks)  Unsupervised learning (kmeans, 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"  
Competencies 
 
Fields of Specialization  
Astrophysics  
Number  Title  Type  ECTS  Hours  Lecturers  
401396100L  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 ...  
Learning objective  
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, largescale structure and weak gravitational lensing.  
Literature  Sugestted textbooks: H. Mo, F. Van den Bosch, S. White: Galaxy Formation and Evolution S. Carroll: SpaceTime 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  
701121600L  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 (chaostheory, ensemble methods); climate models (coupled atmospheric, oceanic and biogeochemical models); climate prediction. Handson 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", 701046100L) as well as experience in programming. Previous experience with PYTHON is useful but not required.  
Chemistry  
Number  Title  Type  ECTS  Hours  Lecturers  
529047400L  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 'handson' 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 manyparticle theory of manyelectron 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 manyparticle quantum mechanics. Derivation of the manyelectron theory for atoms and molecules; starting with the harmonic approximation for the nuclear problem and with HartreeFock theory for the electronic problem to MoellerPlesset perturbation theory and configuration interaction and to coupled cluster and multiconfigurational approaches. Density functional theory. Case studies using quantum mechanical software.  
Lecture notes  Handouts 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/coursesandseminars/exercises/QC_2023.html  
Literature  Textbooks on Quantum Chemistry: F.L. Pilar, Elementary Quantum Chemistry, Dover Publications I.N. Levine, Quantum Chemistry, Prentice Hall HartreeFock in basis set representation: A. Szabo and N. Ostlund, Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory, McGrawHill 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  
227016100L  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 handson 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, Xray, 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  
151020800L  Computational Methods for Flow, Heat and Mass Transfer Problems  W  4 credits  4G  D. W. MeyerMassetti  
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")  
Competencies 

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