Search result: Catalogue data in Spring Semester 2023
Computational Science and Engineering Bachelor  | |||||||||||||||||||||||||||||||||||||||||||||||||||
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| Astrophysics | |||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||
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| 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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| Systems and Control | |||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||
| 227-0216-00L | Computational Control Previously (up until FS22) named "Control Systems II" | W | 6 credits | 2V + 2U | S. Bolognani | ||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The focus of the course is on the design of advanced controllers for cyber-physical systems, that is, systems in which the controller is an embedded computer that can sense and actuate a physical plant. Advanced computational control strategies like Model Predictive Control, Reinforcement Learning, and Data-Driven control will be covered. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The objective of the course is to prepare students to the design of advanced digital control systems: this includes comparing alternative control strategies, deciding what class of controllers to employ for a specific problem, tune the controller in order to meet the desired specifications, and produce a conceptual design of how the controller can be implemented and deployed. Simplifying assumptions on the underlying plant that were made in the course Control Systems are relaxed, and advanced computational control concepts and techniques are presented. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course will cover both the challenges of a digital control system and the many possibilities offered by powerful computation in control. Different aspects and challenges of embedded control of cyber-physical systems will be discussed. We will then review the limitations of classical control strategies like PID control and LQR control, and motivate the need for controllers that employ significant real-time computation. In particular, we will look into Model Predictive Control, Reinforcement Learning, Data-Driven control, and possibly other advanced computational control techniques. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture notes will be available on the Moodle page of the course. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | References to the literature will be provided during the course. No textbook is necessary, but students are encouraged to read the suggested readings. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: Control Systems or equivalent. A background in optimization is very helpful. Students that don’t have it will be provided with some additional reading material. | ||||||||||||||||||||||||||||||||||||||||||||||||||
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| 227-0046-10L | Signals and Systems II | W | 4 credits | 2V + 2U | J. Lygeros | ||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Continuous and discrete time linear system theory, state space methods, frequency domain methods, controllability, observability, stability. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Introduction to basic concepts of system theory. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Modeling and classification of dynamical systems. Modeling of linear, time invariant systems by state equations. Solution of state equations by time domain and Laplace methods. Stability, controllability and observability analysis. Frequency domain description, Bode and Nyquist plots. Sampled data and discrete time systems. Advanced topics: Nonlinear systems, chaos, discrete event systems, hybrid systems. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Copy of transparencies | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Recommended: K.J. Astrom and R. Murray, "Feedback Systems: An Introduction for Scientists and Engineers", Princeton University Press 2009 http://www.cds.caltech.edu/~murray/amwiki/ | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Robotics | |||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||
| 151-0854-00L | Autonomous Mobile Robots | W | 5 credits | 4G | R. Siegwart, L. Ott | ||||||||||||||||||||||||||||||||||||||||||||||
| 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, environment perception, and probabilistic environment modeling, localization, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed across application examples. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning 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, environment perception, and probabilistic environment modeling, localization, 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning 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: http://www.idsc.ethz.ch/education/lectures/recursive-estimation.html | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Requirements: Introductory probability theory and matrix-vector algebra. | ||||||||||||||||||||||||||||||||||||||||||||||||||
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| 252-0579-00L | 3D Vision | W | 5 credits | 3G + 1A | M. Pollefeys, D. B. Baráth | ||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course covers camera models and calibration, feature tracking and matching, camera motion estimation via simultaneous localization and mapping (SLAM) and visual odometry (VO), epipolar and mult-view geometry, structure-from-motion, (multi-view) stereo, augmented reality, and image-based (re-)localization. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | After attending this course, students will: 1. understand the core concepts for recovering 3D shape of objects and scenes from images and video. 2. be able to implement basic systems for vision-based robotics and simple virtual/augmented reality applications. 3. have a good overview over the current state-of-the art in 3D vision. 4. be able to critically analyze and asses current research in this area. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The goal of this course is to teach the core techniques required for robotic and augmented reality applications: How to determine the motion of a camera and how to estimate the absolute position and orientation of a camera in the real world. This course will introduce the basic concepts of 3D Vision in the form of short lectures, followed by student presentations discussing the current state-of-the-art. The main focus of this course are student projects on 3D Vision topics, with an emphasis on robotic vision and virtual and augmented reality applications. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| 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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| Physics | |||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||
| 402-0812-00L | Computational Statistical Physics | W | 8 credits | 2V + 2U | M. Krstic Marinkovic | ||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Computer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning 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. In the first part students learn to apply the following methods: Classical Monte Carlo-simulations, finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Moreover, students learn about the application of statistical physics methods to Boltzmann machines and how to simulate non-equilibrium systems. In the second part, students apply molecular dynamics simulation methods. This part includes long range interactions, Ewald summation and discrete elements. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Computer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture notes and slides are available online and will be distributed if desired. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Literature recommendations and references are included in the lecture notes. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Some basic knowledge about statistical physics, classical mechanics and computational methods is recommended. | ||||||||||||||||||||||||||||||||||||||||||||||||||
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| 402-0810-00L | Computational Quantum Physics Special Students UZH must book the module PHY522 directly at UZH. | W | 8 credits | 2V + 2U | M. H. Fischer | ||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course provides an introduction to simulation methods for quantum systems. Starting from the one-body problem, a special emphasis is on quantum many-body problems, where we cover both approximate methods (Hartree-Fock, density functional theory) and exact methods (exact diagonalization, matrix product states, and quantum Monte Carlo methods). | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Through lectures and practical programming exercises, after this course: Students are able to describe the difficulties of quantum mechanical simulations. Students are able to explain the strengths and weaknesses of the methods covered. Students are able to select an appropriate method for a given problem. Students are able to implement basic versions of all algorithms discussed. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | A script for this lecture will be provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | A list of additional references will be provided in the script. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | A basic knowledge of quantum mechanics, numerical tools (numerical differentiation and integration, linear solvers, eigensolvers, root solvers, optimization), and a programming language (for the teaching assignments, you are free to choose your preferred one). | ||||||||||||||||||||||||||||||||||||||||||||||||||
| 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Computational Finance Offered in the autumn semester. | |||||||||||||||||||||||||||||||||||||||||||||||||||
| Electromagnetics | |||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||
| 227-0707-00L | Optimization Methods for Engineers | W | 3 credits | 2G | J. Smajic | ||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | First half of the semester: Introduction to the main methods of numerical optimization with focus on stochastic methods such as genetic algorithms, evolutionary strategies, etc. Second half of the semester: Each participant implements a selected optimizer and applies it on a problem of practical interest. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Numerical optimization is of increasing importance for the development of devices and for the design of numerical methods. The students shall learn to select, improve, and combine appropriate procedures for efficiently solving practical problems. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Typical optimization problems and their difficulties are outlined. Well-known deterministic search strategies, combinatorial minimization, and evolutionary algorithms are presented and compared. In engineering, optimization problems are often very complex. Therefore, new techniques based on the generalization and combination of known methods are discussed. To illustrate the procedure, various problems of practical interest are presented and solved with different optimization codes. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | PDF of a short skript (39 pages) plus the view graphs are provided | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Lecture only in the first half of the semester, exercises in form of small projects in the second half, presentation of the results in the last week of the semester. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Geophysics Recommended combinations: Subject 1 + Subject 2 Subject 1 + Subject 3 Subject 2 + Subject 3 Subject 3 + Subject 4 Subject 5 + Subject 6 + Subject 8 Subject 4 + Subject 5 Subject 7 + Subject 8 | |||||||||||||||||||||||||||||||||||||||||||||||||||
| 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 | A. Balázs | ||||||||||||||||||||||||||||||||||||||||||||||
| 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||
| 651-4094-00L | Numerical Modelling for Applied Geophysics | W | 4 credits | 3G | J. Robertsson, H. Maurer | ||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Numerical modelling in environmental and exploration geophysics. The course covers different numerical methods such as finite difference and finite element methods applied to solve PDE’s for instance governing seismic wave propagation and geoelectric problems. Prerequisites include basic knowledge of (i) signal processing and applied mathematics such as Fourier analysis and (ii) Matlab. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | After this course students should have a good overview of numerical modelling techniques commonly used in environmental and exploration geophysics. Students should be familiar with the basic principles of the methods and how they are used to solve real problems. They should know advantages and disadvantages as well as the limitations of the individual approaches. The course includes exercises in Matlab where the students both should learn, understand and use existing scripts as well as carrying out some coding in Matlab themselves. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The following topics are covered: - Applications of modelling - Physics of acoustic, elastic, viscoelastic wave equations as well as Maxwell's equations for electromagnetic wave propagation and diffusive problems - Recap of basic techniques in signal processing and applied mathematics - Solving PDE's, boundary conditions and initial conditions - Acoustic/elastic wave propagation I, explicit time-domain finite-difference methods - Acoustic/elastic wave propagation II, Viscoelastic, pseudospectral - Acoustic/elastic wave propagation III, spectral accuracy in time, frequency domain FD, Eikonal - Implicit finite-difference methods (geoelectric) - Finite element methods, 1D/2D (heat equation) - Finite element methods, 3D (geoelectric) - Acoustic/elastic wave propagation IV, Finite element and spectral element methods Most of the lecture 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Igel, H., 2017. Computational seismology: a practical introduction. Oxford University Press. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This course is offered as a half semester course. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Geophysics: Subject 5 offered in the autumn semester | |||||||||||||||||||||||||||||||||||||||||||||||||||
| Geophysics: Subject 6 | |||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||
| 651-4006-00L | Seismic Waves I | W | 3 credits | 3G | S. C. Stähler, D. Kim | ||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Brief review of continuum mechanics and the seismic wave equation; P and S waves; reciprocity and representation theorems; eikonal equation and ray tracing; Huygens and Fresnel; surface-waves; normal-modes; seismic interferometry and noise; numerical solutions. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | After taking this course, students will have the background knowledge necessary to start an original research project in quantitative seismology. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Shearer, P., Introduction to Seismology, Cambridge University Press, 1999. Aki, K. and P. G. Richards, Quantitative Seismology, second edition, University Science Books, Sausalito, 2002. Nolet, G., A Breviary of Seismic Tomography, Cambridge University Press, 2008. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This is a quantitative lecture with an emphasis on mathematical description of wave propagation phenomena on the global scale, hence basic knowledge in vector calculus, linear algebra and analysis as well as seismology (e.g. from the 'wave propagation' lecture) are essential to follow this course. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Geophysics: Subject 7 | |||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||
| 651-4096-00L | Inverse Theory I: Basics | W | 3 credits | 2V | A. Fichtner | ||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Inverse theory is the art of inferring properties of a physical system from noisy and sparse observations. It is used to transform observations of waves into 3D images of a medium seismic tomography, medical imaging and material science; to constrain density in the Earth from gravity; to obtain probabilities of life on exoplanets ... . Inverse theory is at the heart of many natural sciences. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal of this course is to enable students to develop a mathematical formulation of specific inference (inverse) problems that may arise anywhere in the physical sciences, and to implement suitable solution methods. Furthermore, students should become aware that nearly all relevant inverse problems are ill-posed, and that their meaningful solution requires the addition of prior knowledge in the form of expertise and physical intuition. This is what makes inverse theory an art. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This first of two courses covers the basics needed to address (and hopefully solve) any kind of inverse problem. Starting from the description of information in terms of probabilities, we will derive Bayes' Theorem, which forms the mathematical foundation of modern scientific inference. This will allow us to formalise the process of gaining information about a physical system using new observations. Following the conceptual part of the course, we will focus on practical solutions of inverse problems, which will lead us to study Monte Carlo methods and the special case of least-squares inversion. In more detail, we aim to cover the following main topics: 1. The nature of observations and physical model parameters 2. Representing information by probabilities 3. Bayes' theorem and mathematical scientific inference 4. Random walks and Monte Carlo Methods 5. The Metropolis-Hastings algorithm 6. Simulated Annealing 7. Linear inverse problems and the least-squares method 8. Resolution and the nullspace 9. Basic concepts of iterative nonlinear inversion methods While the concepts introduced in this course are universal, they will be illustrated with numerous simple and intuitive examples. These will be complemented with a collection of computer and programming exercises. Prerequisites for this course include (i) basic knowledge of analysis and linear algebra, (ii) basic programming skills, for instance in Matlab or Python, and (iii) scientific curiosity. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Presentation slides and detailed lecture notes will be provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This course is offered as a half-semester course during the first part of the semester | ||||||||||||||||||||||||||||||||||||||||||||||||||
| 651-4096-02L | Inverse Theory II: Applications Prerequisites: The successful completion of 651-4096-00L Inverse Theory I: Basics is mandatory. | W | 3 credits | 2G | A. Fichtner, C. Böhm, A. Zunino | ||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This second part of the course on Inverse Theory provides an introduction to the numerical solution of large-scale inverse problems. Specific examples are drawn from different areas of geophysics and image processing. Students solve various model problems using python and jupyter notebooks, and familiarize themselves with relevant open-source libraries and commercial software. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This course provides numerical tools and recipes to solve (non)-linear inverse problems arising in nearly all fields of science and engineering. After successful completion of the class, the students will have a thorough understanding of suitable solution algorithms, common challenges and possible mitigations to infer parameters that govern large-scale physical systems from sparse data measurements. Prerequisites for this course are (i) 651-4096-00L Inverse Theory: Basics, (ii) basic programming skills. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The class discusses several important concepts to solve (non)-linear inverse problems and demonstrates how to apply them to real-world data applications. All sessions are split into a lecture part in the first half, followed by tutorials using python and jupyter notebooks in the second. The range of covered topics include: 1. Regularization filters and image deblurring 2. Travel-time tomography 3. Line-search methods 4. Time reversal and Born’s approximation 5. Adjoint methods 6. Full-waveform inversion | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Presentation slides and some background material will be provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This course is offered as a half-semester course during the second part of the semester | ||||||||||||||||||||||||||||||||||||||||||||||||||
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