Suchergebnis: Katalogdaten im Frühjahrssemester 2021
Doktorat Departement Bau, Umwelt und Geomatik  Mehr Informationen unter: https://www.ethz.ch/de/doktorat.html | ||||||
 Lehrangebot Doktorat und Postdoktorat | ||||||
| Internationales Doktorandenkolleg "Forschungslabor Raum" Weitere Informationen: www.forschungslabor-raum.info | ||||||
| Weitere Ausbildungsangebote | ||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|---|
| 402-0812-00L | Computational Statistical Physics | W | 8 KP | 2V + 2U | M. Krstic Marinkovic | |
| Kurzbeschreibung | Simulationsmethoden in der statistischen Physik. Klassische Monte-Carlo-Simulationen: finite-size scaling, Clusteralgorithmen, Histogramm-Methoden, Renormierungsgruppe. Anwendung auf Boltzmann-Maschinen. Simulation von Nichtgleichgewichtssystemen. Molekulardynamik-Simulationen: langreichweitige Wechselwirkungen, Ewald-Summation, diskrete Elemente, Parallelisierung. | |||||
| Lernziel | Die Vorlesung ist eine Vertiefung von Simulationsmethoden in der statistischen Physik, und daher ideal als Fortführung der Veranstaltung "Introduction to Computational Physics" des Herbstsemesters. Im ersten Teil lernen Studenten die folgenden Methoden anzuwenden: Klassische Monte-Carlo-Simulationen, finite-size scaling, Clusteralgorithmen, Histogramm-Methoden, Renormierungsgruppe. Ausserdem lernen Studenten die Anwendung der Methoden aus der Statistischen Physik auf Boltzmann-Maschinen kennen und lernen wie Nichtgleichgewichtssysteme simuliert werden. Im zweiten Teil wenden die Studenten Methoden zur Simulation von Molekulardynamiken an. Das beinhaltet unter anderem auch langreichweitige Wechselwirkungen, Ewald-Summation und diskrete Elemente. | |||||
| Inhalt | Simulationsmethoden in der statistischen Physik. Klassische Monte-Carlo-Simulationen: finite-size scaling, Clusteralgorithmen, Histogramm-Methoden, Renormierungsgruppe. Anwendung auf Boltzmann-Maschinen. Simulation von Nichtgleichgewichtssystemen. Molekulardynamik-Simulationen: langreichweitige Wechselwirkungen, Ewald-Summation, diskrete Elemente, Parallelisierung. | |||||
| Skript | Skript und Folien sind online verfügbar und werden bei Bedarf verteilt. | |||||
| Literatur | Literaturempfehlungen und Referenzen sind im Skript enthalten. | |||||
| Voraussetzungen / Besonderes | Grundlagenwissen in der Statistischen Physik, Klassischen Mechanik und im Bereich der Rechnergestützten Methoden ist empfohlen. | |||||
| 151-0906-00L | Frontiers in Energy Research This course is only for doctoral students. | W | 2 KP | 2S | C. Schaffner | |
| Kurzbeschreibung | Doctoral students at ETH Zurich working in the broad area of energy present their research to their colleagues, their advisors and the scientific community. Each week a different student gives a 50-60 min presentation of their research (a full introduction, background & findings) followed by discussion with the audience. | |||||
| Lernziel | The key objectives of the course are: (1) participants will gain knowledge of advanced research in the area of energy; (2) participants will actively participate in discussion after each presentation; (3) participants gain experience of different presentation styles; (4) to create a network amongst the energy research doctoral student community. | |||||
| Inhalt | Doctoral students at ETH Zurich working in the broad area of energy present their research to their colleagues, to their advisors and to the scientific community. There will be one presentation a week during the semester, each structured as follows: 20 min introduction to the research topic, 30 min presentation of the results, 30 min discussion with the audience. | |||||
| Skript | Slides will be available on the Energy Science Center pages(www.esc.ethz.ch/events/frontiers-in-energy-research.html). | |||||
| 101-0178-01L | Uncertainty Quantification in Engineering | W | 3 KP | 2G | S. Marelli, B. Sudret | |
| Kurzbeschreibung | Uncertainty quantification aims at studying the impact of aleatory and epistemic uncertainty onto computational models used in science and engineering. The course introduces the basic concepts of uncertainty quantification: probabilistic modelling of data (copula theory), uncertainty propagation techniques (Monte Carlo simulation, polynomial chaos expansions), and sensitivity analysis. | |||||
| Lernziel | After this course students will be able to properly pose an uncertainty quantification problem, select the appropriate computational methods and interpret the results in meaningful statements for field scientists, engineers and decision makers. The course is suitable for any master/Ph.D. student in engineering or natural sciences, physics, mathematics, computer science with a basic knowledge in probability theory. | |||||
| Inhalt | The course introduces uncertainty quantification through a set of practical case studies that come from civil, mechanical, nuclear and electrical engineering, from which a general framework is introduced. The course in then divided into three blocks: probabilistic modelling (introduction to copula theory), uncertainty propagation (Monte Carlo simulation and polynomial chaos expansions) and sensitivity analysis (correlation measures, Sobol' indices). Each block contains lectures and tutorials using Matlab and the in-house software UQLab (www.uqlab.com). | |||||
| Skript | Detailed slides are provided for each lecture. A printed script gathering all the lecture slides may be bought at the beginning of the semester. | |||||
| Voraussetzungen / Besonderes | A basic background in probability theory and statistics (bachelor level) is required. A summary of useful notions will be handed out at the beginning of the course. A good knowledge of Matlab is required to participate in the tutorials and for the mini-project. | |||||
| 860-0016-00L | Supply and Responsible Use of Mineral Resources II  Number of participants limited to 12. First priority will be given to students enrolled in the Master of Science, Technology, and Policy Program. These students must confirm their participation by 12.02.2021 by registration through myStudies. Students on the waiting list will be notified at the start of the semester. Prerequisite is 860-0015-00 Supply and Responsible Use of Mineral Resources I. | W | 3 KP | 2U | B. Wehrli, F. Brugger, S. Pfister | |
| Kurzbeschreibung | Students integrate their knowledge of mineral resources and technical skills to frame and investigate a commodity-specific challenge faced by countries involved in resource extraction. By own research they evaluate possible policy-relevant solutions, engaging in interdisciplinary teams coached by tutors and experts from natural social and engineering sciences. | |||||
| Lernziel | Students will be able to: - Integrate, and extend by own research, their knowledge of mineral resources from course 860-0015-00, in a solution-oriented team with mixed expertise - Apply their problem solving, and analytical skills to critically assess, and define a complex, real-world mineral resource problem, and propose possible solutions. - Summarize and synthesize published literature and expert knowledge, evaluate decision-making tools, and policies applied to mineral resources. - Document and communicate the findings in concise group presentations and a report. | |||||
| Voraussetzungen / Besonderes | Prerequisite is 860-0015-00 Supply and Responsible Use of Mineral Resources I. Limited to 12 participants. First priority will be given to students enrolled in the Master of Science, Technology, and Policy Program. These students must confirm their participation by February 7th by registration through MyStudies. Students on the waiting list will be notified at the start of the semester. | |||||
| » Auswahl aus sämtlichen Lehrveranstaltungen der ETH Zürich | ||||||
| 102-1248-00L | Experimental Microfluidics: A Short Course Maximale Teilnehmerzahl: 16 | W | 1 KP | 2G | E. Secchi, G. G. Dsouza, S. Stavrakis | |
| Kurzbeschreibung | The course teaches the basics of microfluidic technology and sample a range of applications in microbiology and chemistry, all through hands-on experience and live demos. | |||||
| Lernziel | Familiarization with the basics of microfluidics and with some applications of this technology in microbiology and chemistry. | |||||
| Inhalt | Physics of fluid transport at small scales, design and fabrication of microfluidic devices, set up of a typical microfluidic experiment, flow visualization, image acquisition and analysis, examples of microfluidics studies of chemistry, optofluidic, microbial growth, motility, chemotaxis and interactions among microbes. | |||||
| Skript | Script and papers of previous problems | |||||
| Literatur | Introduction to Microfluidics, Patrick Tabeling, Oxford University Press, 2005 | |||||
| 101-0190-08L | Uncertainty Quantification and Data Analysis in Applied Sciences Findet dieses Semester nicht statt. The course should be open to doctoral students from within ETH and UZH who work in the field of Computational Science. External graduate students and other auditors will be allowed by permission of the instructors. | W | 3 KP | 4G | E. Chatzi, P. Koumoutsakos | |
| Kurzbeschreibung | The course presents fundamental concepts and advanced methodologies for handling and interpreting data in relation with models. It elaborates on methods and tools for identifying, quantifying and propagating uncertainty through models of systems with applications in various fields of Engineering and Applied science. | |||||
| Lernziel | The course is offered as part of the Computational Science Zurich (CSZ) (http://www.zhcs.ch/) graduate program, a joint initiative between ETH Zürich and University of Zürich. This CSZ Block Course aims at providing a graduate level introduction into probabilistic modeling and identification of engineering systems. Along with fundamentals of probabilistic and dynamic system analysis, advanced methods and tools will be introduced for surrogate and reduced order models, sensitivity and failure analysis, parallel processing, uncertainty quantification and propagation, system identification, nonlinear and non-stationary system analysis. | |||||
| Inhalt | The topics to be covered are in three broad categories, with a detailed outline available online (see Learning Materials). Track 1: Uncertainty Quantification and Rare Event Estimation in Engineering, offered by the Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich (18 hours) Lecturers: Prof. Dr. Bruno Sudret, Dr. Stefano Marelli Track 2: Bayesian Inference and Uncertainty Propagation, offered the by the System Dynamics Laboratory, University of Thessaly, and the Chair of Computational Science, ETH Zurich (18 hours) Lecturers: Prof. Dr. Costas Papadimitriou, Dr. Georgios Arampatzis, Prof. Dr. Petros Koumoutsakos Track 3: Data-driven Identification and Simulation of Dynamic Systems, offered the by the Chair of Structural Mechanics, ETH Zurich (18 hours) Lecturers: Prof. Dr. Eleni Chatzi, Dr. Vasilis Dertimanis The lectures will be complemented via a comprehensive series of interactive Tutorials will take place. | |||||
| Skript | The course script is composed by the lecture slides, which will be continuously updated throughout the duration of the course on the CSZ website. | |||||
| Literatur | Suggested Reading: Track 2 : E.T. Jaynes: Probability Theory: The logic of Science Track 3: T. Söderström and P. Stoica: System Identification, Prentice Hall International, Link see Learning Materials. Xiu, D. (2010) Numerical methods for stochastic computations - A spectral method approach, Princeton University press. Smith, R. (2014) Uncertainty Quantification: Theory, Implementation and Applications SIAM Computational Science and Engineering, Lemaire, M. (2009) Structural reliability, Wiley. Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. & Tarantola, S. (2008) Global Sensitivity Analysis - The Primer, Wiley. | |||||
| Voraussetzungen / Besonderes | Introductory course on probability theory Fair command on Matlab | |||||
| 101-0691-00L | Towards Efficient and High-Performance Computing for Engineers | W | 4 KP | 2G | D. Kammer | |
| Kurzbeschreibung | This course is an introduction to various programming techniques and tools for the development of scientific simulations (using C++). It provides the practical and theoretical basis for high-performance computing (HPC) including data structure, testing, performance evaluation and parallelization. The course bridges the gap between introductory and advanced programming courses. | |||||
| Lernziel | This course provides an overview of programming techniques relevant for efficient and high-performance computing. It builds on introductory coding experience (e.g. matlab/python/java) and introduces the students to more advanced tools, specifically C++, external libraries, and supercomputers. The objective of this course is to introduce various approaches of good practice in developing your own code (for your research or engineering project) or using/modifying existing open-source programs. The course targets engineering students and seeks to provide a practical introduction towards performance-based computational simulation. | |||||
| Inhalt | 1. code versioning and DevOps lifecycle 2. introduction to C++ 3. structured programming 4. object-oriented programming 5. code testing 6. code performance (design, data structure, evaluating, using external libraries) 7. code parallelization 8. running simulations on supercomputers | |||||
| Skript | Will be provided during the lecture via moodle. | |||||
| Literatur | Will be provided during the lecture. | |||||
| Voraussetzungen / Besonderes | A good knowledge of MATLAB (or Python or java) is necessary for attending this course. | |||||
| 101-0522-10L | Doctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering Number of participants limited to 21. | W | 1 KP | 2S | B. Soja, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. A. Kraus, M. Lukovic, K. Schindler, M. J. Van Strien | |
| Kurzbeschreibung | Current research in machine learning and data science within the research fields of the department. The goal is to learn about current research projects at our department, to strengthen our expertise and collaboration with respect to data-driven models and methods, to provide a platform where research challenges can be discussed, and also to practice scientific presentations. | |||||
| Lernziel | - learn about discipline-specific methods and applications of data science in neighbouring fields - network people and methodological expertise across disciplines - establish links and discuss connections, common challenges and disciplinespecific differences - practice presentation and discussion of technical content to a broader, less specialised scientific audience | |||||
| Inhalt | Current research at D-BAUG will be presented and discussed. | |||||
| Voraussetzungen / Besonderes | This doctoral seminar is intended for doctoral students affiliated with the Department of Civil, Environmental and Geomatic Engineering. Other students who work on related topics need approval by at least one of the organisers to register for the seminar. Participants are expected to possess elementary skills in statistics, data science and machine learning, including both theory and practical modelling and implementation. The seminar targets students who are actively working on related research projects. | |||||
| 101-0523-11L | Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering (FS21) Number of participants limited to 21. | W | 1 KP | 2S | M. Lukovic, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. A. Kraus, K. Schindler, B. Soja, M. J. Van Strien | |
| Kurzbeschreibung | This doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms). | |||||
| Lernziel | Students will • Critically read scientific papers on the recent developments in machine learning • Put the research in context • Present the contributions • Discuss the validity of the scientific approach • Evaluate the underlying assumptions • Evaluate the transferability/adpatability of the proposed approaches to own research • (Optionally) implement the proposed approaches. | |||||
| Inhalt | With the increasing amount of data collected in various domains, the importance of data science in many disciplines, such as infrastructure monitoring and management, transportation, spatial planning, structural and environmental engineering, has been increasing. The field is constantly developing further with numerous advances, extensions and modifications. The course aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms). Each student will select a paper that is relevant for his/her research and present its content in the seminar, putting it into context, analyzing the assumptions, the transferability and generalizability of the proposed approaches. The students will also link the research content of the selected paper to the own research, evaluating the potential of transferring or adapting it. If possible and applicable, the students will also implement the adapted algorithms The students will work in groups of three students, where each of the three students will be reading each other’s selected papers and providing feedback to each other. | |||||
| Voraussetzungen / Besonderes | This doctoral seminar is intended for doctoral students affiliated with the Department of Civil, Environmental and Geomatic Engineering. Other students who work on related topics need approval by at least one of the organisers to register for the seminar. Participants are expected to possess elementary skills in statistics, data science and machine learning, including both theory and practical modelling and implementation. The seminar targets students who are actively working on related research projects. | |||||
| 101-0190-10L | Eddy-resolving Numerical Simulations and Coherent Structures in Hydrodynamics and Env. Hydraulics | W | 2 KP | 1.3G | S. G. Constantinescu | |
| Kurzbeschreibung | The course requires no background in numerical methods and CFD and it addressed to graduate students and scientists with background in both experimental and numerical research. | |||||
| Lernziel | The main purpose of the short course is to present an introduction to coherent structures and their role in explaining the physics of several important categories of flows relevant to hydrodynamics of rivers and environmental hydraulics. The focus is on examples related to hydraulics, river engineering and stratified flows. The relationship between flow instabilities and coherent structures is emphasized. The use of fully three-dimensional eddy-resolving numerical simulations offers a great instrument for the study of the dynamics of the coherent structures. The course presents an introduction to the main types of eddy-resolving numerical methods (Unsteady RANS, Large Eddy Simulation, Direct Numerical Simulation) and discusses the advantages and disadvantages of these methods, and the numerical requirements related to obtaining accurate solutions. Emphasis is put on hybrid RANS-LES methods and advanced wall models that allow simulating environmental flows at field conditions and study of scale effects. The main goal of the course is to familiarize the participants with the main ideas and concepts behind eddy resolving methods, their advantages & limitations and to provide examples that show how the results of such simulations can advance our understanding of the relevant processes and mechanisms controlling transport of heat, contaminant and sediment in turbulent flows, turbulent diffusion and dispersion with special emphasis on fluvial processes. | |||||
| Inhalt | L1. Introduction to turbulence modeling (DNS, LES, RANS) Introduction to LES modeling (Part 1) L2. Introduction to LES modeling (Part 2) Wall models and hybrid RANS-LES methods (DES) L3. Flow instabilities and coherent structures In-depth example: open channel flow over 2-D dunes L4. Flow and sediment erosion mechanisms around surface-mounted solid and porous cylinders (bridge piers, patches of emerged or submerged vegetation). Shallow wakes past cylinders L5. Gravity currents: Introduction and main flow regimes Gravity currents propagating in a porous environment (e.g., vegetated or partially vegetated channel) Gravity currents propagating over large-scale roughness elements (e.g., dunes) Gravity currents propagating over an inclined flat surface Gravity currents propagating over cyclic steps L6. Shallow mixing interfaces between parallel and non-parallel streams with focus on river confluences and groyne fields, and temperature stratification effects L7. Dispersion of buoyant and non-buoyant contaminants from bottom river cavities Flow hydrodynamics and mixing in stratified lakes due to wind forcing L8. Flow past isolated freshwater mussels and mussel beds Pump-intake vortices L9. Class evaluation via a closed book 1.15 hours written exam | |||||
| Skript | PDF files of the material presented in the lectures will be provided | |||||
| Literatur | Recommended textbook: Rodi, W, Constantinescu, G. and Stoesser, T. (2013) “Large Eddy Simulation in hydraulics” IAHR Monograph, CRC Press, Taylor & Francis Group (ISBN-10: 1138000247) 310 pages | |||||
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