Suchergebnis: Katalogdaten im Frühjahrssemester 2018

Doktorat Departement Bau, Umwelt und Geomatik Information
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Lehrangebot Doktorat und Postdoktorat
Weitere Ausbildungsangebote
NummerTitelTypECTSUmfangDozierende
402-0812-00LComputational Statistical Physics Information W8 KP2V + 2UH. J. Herrmann
KurzbeschreibungSimulationsmethoden in der statistischen Physik. Klassische Monte-Carlo-Simulationen: finite-size scaling, Clusteralgorithmen, Histogramm-Methoden. Molekulardynamik-Simulationen: langreichweitige Wechselwirkungen, Ewald-Summation, diskrete Elemente, Parallelisierung.
LernzielDie Vorlesung ist eine Vertiefung von Simulationsmethoden in der statistischen Physik, und daher ideal als Fortführung der Veranstaltung "Introduction to Computational Physics" des Herbstsemesters mit folgenden Schwerpunkten. Klassische Monte-Carlo-Simulationen: finite-size scaling, Clusteralgorithmen, Histogramm-Methoden. Molekulardynamik-Simulationen: langreichweitige Wechselwirkungen, Ewald-Summation, diskrete Elemente, Parallelisierung.
InhaltSimulationsmethoden in der statistischen Physik.
Klassische Monte-Carlo-Simulationen: finite-size scaling, Clusteralgorithmen, Histogramm-Methoden. Molekulardynamik-Simulationen: langreichweitige Wechselwirkungen, Ewald-Summation, diskrete Elemente, Parallelisierung.
151-0906-00LFrontiers in Energy Research Belegung eingeschränkt - Details anzeigen
This course is only for doctoral students.
W2 KP2SD. Poulikakos, R. Boes, V. Hoffmann, G. Hug, M. Mazzotti, A. Patt, A. Schlüter
KurzbeschreibungDoctoral 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.
LernzielKnowledge of advanced research in the area of energy.
InhaltDoctoral 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.
SkriptSlides will be available on the Energy Science Center pages(Link).
101-0178-01LUncertainty Quantification in Engineering Information W3 KP2GB. Sudret, S. Marelli
KurzbeschreibungUncertainty 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.
LernzielAfter 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.
InhaltThe 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 (Link).
SkriptDetailed slides are provided for each lecture. A printed script gathering all the lecture slides may be bought at the beginning of the semester.
Voraussetzungen / BesonderesA 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.
» Auswahl aus sämtlichen Lehrveranstaltungen der ETH Zürich
101-0190-09LSelected Topics on Modeling Nonlinear Dynamical SystemsW2 KP4VJ.‑S. Pei
KurzbeschreibungIn this Ph.D.-level course, some active research topics on modeling nonlinear dynamical systems will be introduced and demonstrated through numerical exercises.
LernzielThe focused applications are given to piece-wise smooth systems, and non-stationary and nonlinear signals that challenge routine research practice. The selected topics include state event location algorithms for numerical integration, memristive/memcapacitive "mem" models for hysteresis in engineering mechanics, as well as neural networks for function approximation.
101-0190-08LUncertainty Quantification and Data Analysis in Applied Sciences Information
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.
W3 KP4GE. Chatzi, P. Chatzidoukas, P. Koumoutsakos, S. Marelli, V. Ntertimanis, K. Papadimitriou, B. Sudret
KurzbeschreibungThe 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.
LernzielThe course is offered as part of the Computational Science Zurich (CSZ) (Link) 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.
InhaltThe 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 (20 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 (20 hours)
Lecturers: Prof. Dr. Costas Papadimitriou, Dr. Panagiotis Hadjidoukas, Prof. Dr. Petros Koumoutsakos
Track 3: Data-driven Identification and Simulation of Dynamic Systems, offered the by the Chair of Structural Mechanics, ETH Zurich (20 hours)
Lecturers: Prof. Dr. Eleni Chatzi, Dr. Vasilis Dertimanis.
The lectures will be complemented via a comprehensive series of interactive Tutorials will take place.
SkriptThe course script is composed by the lecture slides, which will be continuously updated throughout the duration of the course on the CSZ website.
LiteraturSuggested 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 / BesonderesIntroductory course on probability theory
Fair command on Matlab
860-0016-00LSupply and Responsible Use of Mineral Resources II Belegung eingeschränkt - Details anzeigen
Maximale Teilnehmerzahl: 12

The students must be enrolled in 860-0015-00 Supply and Responsible Use of Mineral Resources I. The course is limited to 12 participants, and the students will compose two teams of mixed background and expertise. 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 8th by registration through MyStudies. Other graduate students interested in enrolling will be placed onto a waiting list when registering through MyStudies and will be provided with confirmation after February 8th
W3 KP2UB. Wehrli, F. Brugger, A. Gilli, C. A. Heinrich, C. Karydas, N. Lefebvre
KurzbeschreibungStudents 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.
LernzielStudents 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 / BesonderesPrerequisite 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 12th by registration through MyStudies. We will try to accommodate all other interested graduate students, however you will be placed onto a waiting list when registering through MyStudies and will be provided with confirmation shortly after February 12th.
All Wednesday meetings are mandatory contact time.
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