Search result: Catalogue data in Spring Semester 2016
Computational Science and Engineering Bachelor | ||||||
Fields of Specialization | ||||||
Astrophysics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
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402-0394-00L | Theoretical Astrophysics and Cosmology | W | 10 credits | 4V + 2U | L. M. Mayer, A. Refregier | |
Abstract | This is the second of a two course series which starts with "General Relativity" and continues in the spring with "Theoretical Astrophysics and Cosmology", where the focus will be on applying general relativity to cosmology as well as developing the modern theory of structure formation in a cold dark matter Universe. | |||||
Objective | ||||||
Content | The course will cover the following topics: - Homogeneous cosmology - Thermal history of the universe, recombination, baryogenesis and nucleosynthesis - Dark matter and Dark Energy - Inflation - Perturbation theory: Relativistic and Newtonian - Model of structure formation and initial conditions from Inflation - Cosmic microwave background anisotropies - Spherical collapse and galaxy formation - Large scale structure and cosmological probes | |||||
Literature | Suggested textbooks: H.Mo, F. Van den Bosch, S. White: Galaxy Formation and Evolution S. Carroll: Space-Time and Geometry: An Introduction to General Relativity S. Dodelson: Modern Cosmology Secondary textbooks: S. Weinberg: Gravitation and Cosmology V. Mukhanov: Physical 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. Lyth: Cosmological Inflation and Large Scale Structure | |||||
Prerequisites / Notice | Knowledge of General Relativity is recommended. | |||||
Physics of the Atmosphere | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
701-1216-00L | Numerical Modelling of Weather and Climate | W | 4 credits | 3G | C. Schär, U. Lohmann | |
Abstract | The guiding principle of this lecture is that students can understand how weather and climate models are formulated from the governing physical principles and how they are used for climate and weather prediction purposes. | |||||
Objective | The guiding principle of this lecture is that students can 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 numerical methods (e.g., "Numerische Methoden in der Umweltphysik", 701-0461-00L) | |||||
Chemistry | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
529-0474-00L | Quantum Chemistry | W | 6 credits | 3G | M. Reiher | |
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. | |||||
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 will be provided for each lecture (this script has been completely revised in spring 2014 anf has been supplemented by (computer) examples that continuously illustrate how the theory works). | |||||
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 | |||||
Fluid Dynamics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
151-0208-00L | Computational Methods for Flow, Heat and Mass Transfer Problems | W | 4 credits | 2V + 2U | P. Jenny | |
Abstract | Numerical methods for the solution of flow, heat and mass transfer problems are presented and practised by analytical and computer solutions for simple examples. Subjects: solution process, physical and mathematical models, basic equations, discretization methods, numerical solution of advection, diffusion and Poisson equations, turbulent flows. | |||||
Objective | Knowledge of and practical experience with important discretisation and solution methods for Computational Fluid Dynamics, Heat and Mass Transfer Problems | |||||
Content | Aufbauend auf den Lehrveranstaltungen über Fluiddynamik, Thermodynamik, Computational Methods for Engineering Application I (empfehlenswertes Wahlfach, 4. Semester) und Informatik (Programmieren) werden numerische Methoden für Berechnungsaufgaben der Fluiddynamik, Energie- und Verfahrenstechnik dargestellt und an einfachen Beispielen geübt. 1. Einleitung Uebersicht, Anwendungen Problemlösungsprozess, Fehler 2. Rekapitulation der Grundgleichungen Formulierung, Anfangs- und Randbedingungen 3. Numerische Diskretisierungsverfahren Finite-Differenzen- und Finite-Volumen-Verfahren Grundbegriffe: Konsistenz, Stabilität, Konvergenz 4. Lösung der grundlegenden Gleichungstypen Wärmeleitungs/Diffusionsgleichung (parabolisch) Poisson-Gleichung (elliptisch) Advektionsgleichung/Wellengleichung (hyperbolisch) und Advektions-Diffusions-Gleichung 5. Berechnung inkompressibler Strömungen 6. Berechnung turbulenter Strömungen | |||||
Lecture notes | Lecture notes are available (in German) | |||||
Literature | a list of references is supplied | |||||
Prerequisites / Notice | It is crucial to actively solve the analytical and practical (programming) exercises. | |||||
Systems and Control | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0216-00L | Control Systems II | W | 6 credits | 4G | R. Smith | |
Abstract | Introduction to basic and advanced concepts of modern feedback control. | |||||
Objective | Introduction to basic and advanced concepts of modern feedback control. | |||||
Content | This course is designed as a direct continuation of the course "Regelsysteme" (Control Systems). The primary goal is to further familiarize students with various dynamic phenomena and their implications for the analysis and design of feedback controllers. Simplifying assumptions on the underlying plant that were made in the course "Regelsysteme" are relaxed, and advanced concepts and techniques that allow the treatment of typical industrial control problems are presented. Topics include control of systems with multiple inputs and outputs, control of uncertain systems (robustness issues), limits of achievable performance, and controller implementation issues. | |||||
Lecture notes | The slides of the lecture are available to download | |||||
Literature | Skogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005. | |||||
Prerequisites / Notice | Prerequisites: Control Systems or equivalent | |||||
Robotics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
151-0854-00L | Autonomous Mobile Robots | W | 5 credits | 4G | R. Siegwart, M. Chli, M. Rufli | |
Abstract | The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed accross application examples. | |||||
Objective | The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation. | |||||
Lecture notes | This lecture is enhanced by around 30 small videos introducing the core topics, and multiple-choice questions for continuous self-evaluation. It is developed along the TORQUE (Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness) concept, which is ETH's response to the popular MOOC (Massive Open Online Course) concept. | |||||
Literature | This lecture is based on the Textbook: Introduction to Autonomous Mobile Robots Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza, The MIT Press, Second Edition 2011, ISBN: 978-0262015356 | |||||
151-0566-00L | Recursive Estimation | W | 4 credits | 2V + 1U | R. D'Andrea | |
Abstract | Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way. | |||||
Objective | Learn the basic recursive estimation methods and their underlying principles. | |||||
Content | Introduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle. | |||||
Lecture notes | Lecture notes available on course website: Link | |||||
Prerequisites / Notice | Requirements: Introductory probability theory and matrix-vector algebra. | |||||
252-0220-00L | Learning and Intelligent Systems | W | 8 credits | 4V + 2U + 1A | A. Krause | |
Abstract | The course introduces the foundations of learning and making predictions based on data. | |||||
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-NN - 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 networks and exact inference (conditional independence; variable elimination; TANs) - Approximate inference (sum/max product; Gibbs sampling) - Latent variable models (Gaussian Misture Models, EM Algorithm) - Temporal models (Bayesian filtering, Hidden Markov Models) - Sequential decision making (MDPs, value and policy iteration) - Reinforcement learning (model-based RL, Q-learning) | |||||
Literature | Textbook: Kevin Murphy: A Probabilistic Perspective, MIT Press | |||||
Prerequisites / Notice | Designed to provide basis for following courses: - Advanced Machine Learning - Data Mining: Learning from Large Data Sets - Probabilistic Artificial Intelligence - Probabilistic Graphical Models - Seminar "Advanced Topics in Machine Learning" | |||||
Physics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
402-0812-00L | Computational Statistical Physics | W | 8 credits | 2V + 2U | H. J. Herrmann | |
Abstract | Computer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization. | |||||
Objective | The lecture will give a deeper insight into computer simulation methods in statistical physics. Thus, it is an ideal continuation of the lecture "Introduction to Computational Physics" of the autumn semester focusing on the following topics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization. | |||||
Content | Computer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization. | |||||
402-0810-00L | Computational Quantum Physics | W | 8 credits | 2V + 2U | S. Huber | |
Abstract | This course provides an introduction to simulation methods for quantum systems, starting with the one-body problem and finishing with quantum field theory, with special emphasis on quantum many-body systems. Both approximate methods (Hartree-Fock, density functional theory) and exact methods (exact diagonalization, quantum Monte Carlo) are covered. | |||||
Objective | The goal is to become familiar with computer simulation techniques for quantum physics, through lectures and practical programming exercises. | |||||
327-5102-00L | Molecular and Materials Modelling | W | 4 credits | 2V + 2U | J. VandeVondele, D. Passerone | |
Abstract | "Molecular and Materials Modelling" introduces the basic techniques to interpret experiments with contemporary atomistic simulation. These techniques include force fields or density functional theory (DFT) based molecular dynamics and Monte Carlo. Structural and electronic properties, thermodynamic and kinetic quantities, and various spectroscopies will be simulated for nanoscale systems. | |||||
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, STM, 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. | |||||
Lecture notes | A script will be made available. | |||||
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. Andrew R. Leach, Molecular Modelling, principles and applications, Pearson, 2001 | |||||
Computational Finance No course offerings in this semester. | ||||||
Electromagnetics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
227-0366-00L | Introduction to Computational Electromagnetics | W | 6 credits | 4G | C. Hafner, J. Leuthold, J. Smajic | |
Abstract | An overview over the most prominent methods for the simulation of electromagnetic fields is given This includes domain methods such as finite differences and finite elements, method of moments, and boundary methods. Both time domain and frequency domain techniques are considered. | |||||
Objective | Overview of numerical methods for the simulation of electromagnetic fields and hands-on experiments with selected methods. | |||||
Content | Overview of concepts of the main numerical methods for the simulation of electromagnetic fields: Finite Difference Method, Finite Element Method, Transmission Line Matrix Method, Matrix Methods, Multipole Methods, Image Methods, Method of Moments, Integral Equation Methods, Beam Propagation Method, Mode Matching Technique, Spectral Domain Analysis, Method of Lines. Applications: Problems in electrostatic and magnetostatic, guided waves and free-space propagation problems, antennas, resonators, inhomogeneous transmissionlLines, nanotechnic, optics etc. | |||||
Lecture notes | Download from: Link | |||||
Prerequisites / Notice | First half of the semester: lectures; second half of the semester: exercises in form of small projects | |||||
Geophysics Recommended combinations: Subject 1 + Subject 2 Subject 1 + Subject 3 Subject 2 + Subject 3 Subject 3 + Subject 4 Subject 5 + Subject 6 Subject 5 + Subject 4 | ||||||
Geophysics: Subject 1 offered in the autumn semester | ||||||
Geophysics: Subject 2 offered in the autumn semester | ||||||
Geophysics: Subject 3 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
651-4008-00L | Dynamics of the Mantle and Lithosphere | W | 3 credits | 2G | D. A. May | |
Abstract | The goal of this course is to obtain a detailed understanding of the physical properties, structure, and dynamical behavior of the mantle-lithosphere system, focusing mainly on Earth but also discussing how these processes occur differently in other terrestrial planets. | |||||
Objective | The goal of this course is to obtain a detailed understanding of the physical properties, structure, and dynamical behavior of the mantle-lithosphere system, focusing mainly on Earth but also discussing how these processes occur differently in other terrestrial planets. | |||||
Geophysics: Subject 4 recognition requires the successful completion of both course units | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
651-4094-00L | Numerical Modelling for Applied Geophysics I | W | 3 credits | 2G | J. Robertsson | |
Abstract | This course provides an introduction to numerical modelling techniques as they are employed in many projects in Applied Geophysics. The focus is rather on the basic principles and applications than on rigorous mathematical proofs. Prerequisites for this course include (i) basic knowledge of vector analysis and Fourier transform techniques and (ii) knowledge of Matlab (required for the exercises). | |||||
Objective | After this course the students should have a good overview of the numerical modelling techniques that are commonly applied in Applied Geophysics. They should be familiar with the basic principles of the methods. Furthermore, they should know advantages and disadvantages as well as the limitations of the individual approaches. | |||||
Content | During the first part of the course, the following topics are covered: - General issues about finite precision of numerical modeling - Potential field modeling - Layered Earth modeling using transform methods - Finite differences - Finite elements - Other numerical methods Most of these modules are accompanied by exercises Small projects will be assigned to the students. They either include a programming exercise or applications of existing modelling codes. | |||||
Lecture notes | Presentation slides and some background material will be provided. | |||||
Prerequisites / Notice | This course is offered as a half-semester course during the first part of the semester | |||||
651-4096-00L | Inverse Theory for Geophysics I: Basics | W | 3 credits | 2V | H. Maurer, A. Fichtner | |
Abstract | This course provides an introduction to inversion theory. The focus is rather on the basic principles and applications than on rigorous mathematical proofs. Prerequisites for this course include (i) basic knowledge of analysis and linear algebra and (ii) knowledge of Matlab (required for the exercises). | |||||
Objective | After this course the students should have a good grasp of geophysical inversion problems. In particular, they should be familiar with linear and non-linear inversion techniques. Most importantly, they should be aware of potential pitfalls and limitations of the methods. | |||||
Content | During this course, the following topics are covered: - Introduction to geophysical inversion - Matrix inversion techniques - Linear inversion problems - Non-linear inversion problems - Probabilistic inversion approaches - Global optimizers Most of these modules are accompanied by exercises | |||||
Lecture notes | Presentation slides and some background material will be provided. | |||||
Prerequisites / Notice | This course is offered as a half-semester course during the first part of the semester | |||||
Geophysics: Subject 5 offered in the autumn semester | ||||||
Geophysics: Subject 6 | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
651-4006-00L | Seismology of the Spherical Earth | W | 3 credits | 2G | A. Fichtner, M. van Driel | |
Abstract | Brief review of continuum mechanics and earthquake modeling. Approaches to solving the momentum equation in realistic Earth models, or ways to calculate a theoretical seismogram: homogeneous wave equation; P and S waves; eikonal equation and ray tracing; surface-wave solutions; normal-mode solutions; numerical solutions. | |||||
Objective | After taking this course, students will have the background knowledge necessary to start an original research project in global theoretical seismology. | |||||
Literature | Aki, K. and P. G. Richards, Quantitative Seismology, second edition, University Science Books, Sausalito, 2002. Dahlen, F. A. and J. Tromp, Theoretical Global Seismology, Princeton University Press, Princeton, 1998. Lay, T. and T. C. Wallace, Modern Global Seismology, Academic Press, San Diego, 1995. Shearer, P., Introduction to Seismology, Cambridge University Press, 1999. Udias, A., Principles of Seismology, Cambridge University Press, 1999. | |||||
Biology | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
636-0702-00L | Statistical Models in Computational Biology Does not take place this semester. | W | 5 credits | 2V + 1U | N. Beerenwinkel | |
Abstract | The course offers an introduction to graphical models and their application to complex biological systems. Graphical models combine a statistical methodology with efficient algorithms for inference in settings of high dimension and uncertainty. The unifying graphical model framework is developed and used to examine several classical and topical computational biology methods. | |||||
Objective | The goal of this course is to establish the common language of graphical models for applications in computational biology and to see this methodology at work for several real-world data sets. | |||||
Content | Graphical models are a marriage between probability theory and graph theory. They combine the notion of probabilities with efficient algorithms for inference among many random variables. Graphical models play an important role in computational biology, because they explicitly address two features that are inherent to biological systems: complexity and uncertainty. We will develop the basic theory and the common underlying formalism of graphical models and discuss several computational biology applications. Topics covered include conditional independence, Bayesian networks, Markov random fields, Gaussian graphical models, EM algorithm, junction tree algorithm, model selection, Dirichlet process mixture, causality, the pair hidden Markov model for sequence alignment, probabilistic phylogenetic models, phylo-HMMs, microarray experiments and gene regulatory networks, protein interaction networks, learning from perturbation experiments, time series data and dynamic Bayesian networks. Some of the biological applications will be explored in small data analysis problems as part of the exercises. | |||||
Lecture notes | no | |||||
Literature | - Airoldi EM (2007) Getting started in probabilistic graphical models. PLoS Comput Biol 3(12): e252. doi:10.1371/journal.pcbi.0030252 - Bishop CM. Pattern Recognition and Machine Learning. Springer, 2007. - Durbin R, Eddy S, Krogh A, Mitchinson G. Biological Sequence Analysis. Cambridge university Press, 2004 | |||||
636-0706-00L | Spatio-Temporal Modelling in Biology | W | 5 credits | 3G | D. Iber | |
Abstract | This course focuses on modeling spatio-temporal problems in biology, in particular on the cell and tissue level. A wide range of mathematical techniques will be presented as part of the course, including concepts from non-linear dynamics (ODE and PDE models), stochastic techniques (SDE, Master equations, Monte Carlo simulations), and thermodynamic descriptions. | |||||
Objective | The aim of the course is to introduce students to state-of-the-art mathematical modelling of spatio-temporal problems in biology. Students will learn how to chose from a wide range of modelling techniques and how to apply these to further our understanding of biological mechanisms. The course aims at equipping students with the tools and concepts to conduct successful research in this area; both classical as well as recent research work will be discussed. | |||||
Content | 1. Introduction to Modelling in Biology 2. Morphogen Gradients 3. Turing Pattern 4. Travelling Waves & Wave Pinning 5. Application Example 1: Dorso-ventral axis formation 6. Chemotaxis, Cell Adhesion & Migration 7. Introduction to Numerical Methods 8. Simulations on Growing Domains 9. Image-Based Modelling 10. Branching Processes 11. Cell-based Simulation Frameworks 12. Application Example 2: Limb Development 13. Summary | |||||
Lecture notes | All lecture material will be made available online Link | |||||
Literature | Murray, Mathematical Biology, Springer Forgacs and Newman, Biological Physics of the Developing Embryo, CUP Keener and Sneyd, Mathematical Physiology, Springer Fall et al, Computational Cell Biology, Springer Szallasi et al, System Modeling in Cellular Biology, MIT Press Wolkenhauer, Systems Biology Kreyszig, Engineering Mathematics, Wiley | |||||
Prerequisites / Notice | The course builds on introductory courses in Computational Biology. The course assumes no background in biology but a good foundation regarding mathematical and computational techniques. |
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