Suchergebnis: Katalogdaten im Frühjahrssemester 2021
Bauingenieurwissenschaften Master | ||||||
Master-Studium (Studienreglement 2020) | ||||||
Fächer Digital | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
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101-0138-11L | Bridge Design: Project Competition Number of participants limited to 20. All students get on waiting list. Final registration based on application letter (information given in first lecture). Priority will be given to students attending “Bridge Design (101-0138-00 G)” and in the primary target group (Major in Structural Engineering or Projektbasierte Lehrveranstaltungen). | W | 4 KP | 2S | W. Kaufmann | |
Kurzbeschreibung | This module offers the possibility to apply the fundamentals of the course Bridge Design in a conceptual design project. The scenario is set as a design competition: The students (group of two) will get a basic documentation (service criteria agreement, plans, digital terrain model, geotechnical report, photo documentation, etc.) and will develop a conceptual design suitable for the given site. | |||||
Lernziel | At the end of the course, students will have developed a convincing bridge design that satisfies following criteria: _ Consideration of governing boundary conditions and constraints. _ Conception of an efficient structural system with an adequate aesthetic expression considering the environment. _ Definition of the relevant actions and decisive load cases. _ Proof of feasibility by dimensioning the main structural elements. _ Schematic overview of construction processes. _ Appropriate presentation and visualisation of the proposed bridge design. | |||||
Inhalt | The module is built up as follows: 0. Presentation of problem statement / project. (1st week of semester) 1. Team registration (teams of two students). 2. Issue of documents. 3. Introduction to design tools & working methods. 4. Working on project (milestones): ... a. Define requirements and boundary conditions. ... b. Study of references and possible concepts ... c. Choice of best variant ... d. Structural modelling & calculations ... e. Plans & visualisation 5. Submission | |||||
Voraussetzungen / Besonderes | It is highly recommended to attend the course “Bridge Design (101-0138-00 G)” simultaneously. | |||||
101-0579-00L | Infrastructure Management 2: Evaluation Tools | W | 6 KP | 2G | B. T. Adey, S. Kerwin, S. Moghtadernejad | |
Kurzbeschreibung | This course provides tools to predict the service being provided by infrastructure in situations where the infrastructure is expected to 1) to evolve slowly with relatively little uncertainty over time, e.g. due to the corrosion of a metal bridge, and 2) to change suddenly with relatively large uncertainty, e.g. due to being washed away from an extreme flood. | |||||
Lernziel | The course learning objective is to equip students with tools to be used to the service being provided from infrastructure. The course increases a student's ability to analyse complex problems and propose solutions and to use state-of-the-art methods of analysis to assess complex problems | |||||
Inhalt | Reliability Availability and maintainability Regression analysis Event trees Fault trees Markov chains Neural networks Bayesian networks | |||||
Skript | All necessary materials (e.g. transparencies and hand-outs) will be distributed before class. | |||||
Literatur | Appropriate reading material will be assigned when necessary. | |||||
Voraussetzungen / Besonderes | Although not an official prerequisite, it is perferred that students have taken the IM1:Process course first. Understanding of the infrastructure management process enables a better understanding of where and how the tools introduced in this course can be used in the management of infrastructure. | |||||
101-0523-00L | Industrialized Construction | W | 4 KP | 3G | D. Hall | |
Kurzbeschreibung | This course offers an introduction and overview to Industrialized Construction, a rapidly-emerging concept in the construction industry. The course will present the driving forces, concepts, technologies, and managerial aspects of Industrialized Construction, with an emphasis on current industry applications and future entrepreneurial opportunities in the field. | |||||
Lernziel | By the end of the course, students should be able to: 1. Describe the characteristics of the nine integrated areas of industrialized construction: planning and control of processes; developed technical systems; prefabrication; long-term relations; logistics; use of ICT; re-use of experience and measurements; customer and market focus; continuous improvement. 2. Assess case studies on successful or failed industry implementations of industrialized construction in Europe, Japan and North America. 3. Propose a framework for a new industrialized construction company for a segment of the industrialized construction market (e.g. housing, commercial, schools) including the company’s business model, technical platform, and supply chain strategy. 4. Identify future trends in industrialized construction including the use of design automation, digital fabrication, and Industry 4.0. | |||||
Inhalt | The application of Industrialized Construction - also referred to as prefabrication, offsite building, or modular construction – is rapidly increasing in the industry. Although the promise of industrialized construction has long gone unrealized, several market indicators show that this method of construction is quickly growing around the world. Industrialized Construction offers potential for increased productivity, efficiency, innovation, and safety on the construction site. The course will present the driving forces, concepts, technologies, and managerial aspects of Industrialized Construction. The course unpacks project-orientated vs. product-oriented approaches while showcasing process and technology platforms used by companies in Europe, the UK, Japan, and North America. The course highlights future business models and entrepreneurial opportunities for new industrialized construction ventures. The course is organized around a group project carried out in teams of 3-4. Each specific class will include some theory about industrialized construction from a strategic and/or technological perspective. There will be several external guest lectures as well. During the last hour of the course, students will work in project teams to propose a framework for a new industrialized construction venture. The teams will need to determine their new company’s product offering, business model, technical platform, technology solutions, and supply chain strategy. It is intended to hold a group excursion to a factory for a 1/2 day visit. However in 2021, this will be determined pending the status of COVID-19 restrictions. planned course activities include a 1/2 day factory visit Students who are unable to attend the visit can make up participation through independent research and the writing of a short paper. | |||||
Literatur | A full list of required readings will be made available to the students via Moodle. | |||||
101-0521-10L | Machine Learning for Predictive Maintenance Applications The number of participants in the course is limited to 25 students. Students interested in attending the lecture are requested to upload their transcript and a short motivation responding the following two questions (max. 200 words): -How does this course fit to the other courses you have attended so far? -How does the course support you in achieving your goal? The following link can be used to upload the documents. Link | W | 8 KP | 4G | O. Fink | |
Kurzbeschreibung | The course aims at developing machine learning algorithms that are able to use condition monitoring data efficiently and detect occurring faults in complex industrial assets, isolate their root cause and ultimately predict the remaining useful lifetime. | |||||
Lernziel | Students will - be able to understand the main challenges faced by predictive maintenance systems - learn to extract relevant features from condition monitoring data -learn to select appropriate machine learning algorithms for fault detection, diagnostics and prognostics -learn to define the learning problem in way that allows its solution based on existing constrains such as lack of fault samples. - learn to design end-to-end machine learning algorithms for fault detection and diagnostics -be able to evaluate the performance of the applied algorithms. At the end of the course, the students will be able to design data-driven predictive maintenance applications for complex engineered systems from raw condition monitoring data. | |||||
Inhalt | Early and reliable detection, isolation and prediction of faulty system conditions enables the operators to take recovery actions to prevent critical system failures and ensure a high level of availability and safety. This is particularly crucial for complex systems such as infrastructures, power plants and aircraft engines. Therefore, their system condition is increasingly tightly monitored by a large number of diverse condition monitoring sensors. With the increased availability of data on system condition on the one hand, and the increased complexity of explicit system physics-based models on the other hand, the application of data-driven approaches for predictive maintenance has been recently increasing. This course provides insights and hands-on experience in selecting, designing, optimizing and evaluating machine learning algorithms to tackle the challenges faced by maintenance systems of complex engineered systems. Specific topics include: -Introduction to condition monitoring and predictive maintenance systems -Feature extraction and selection methodology -Machine learning algorithms for fault detection and fault isolation -End-to-end learning architectures (including feature learning) for fault detection and fault isolation -Unsupervised and semi-supervised learning algorithms for predictive maintenance -Machine learning algorithms for prediction of the remaining useful life -Performance evaluation -Predictive maintenance systems at fleet level -Domain adaptation for fault diagnostics -Introduction to decision support systems for maintenance applications | |||||
Skript | Slides and other materials will be available online. | |||||
Literatur | Relevant scientific papers will be discussed in the course. | |||||
Voraussetzungen / Besonderes | Strong analytical skills. Programming skills in python are strongly recommended. | |||||
101-0158-01L | Method of Finite Elements I | W | 4 KP | 2G | E. Chatzi, P. Steffen | |
Kurzbeschreibung | The course introduces students to the fundamental concepts of the Method of Finite Elements, including element formulations, numerical solution procedures and modelling details. We aim to equip students with the ability to code algorithms (based on Python) for the solution of practical problems of structural analysis. DISCLAIMER: the course is not an introduction to commercial software. | |||||
Lernziel | The Direct Stiffness Method is revisited and the basic principles of Matrix Structural Analysis are overviewed. The basic theoretical concepts of the Method of Finite Elements are imparted and perspectives for problem solving procedures are provided. Linear finite element models for truss and continuum elements are introduced and their application for structural elements is demonstrated. The Method of Finite Elements is implemented on practical problems through accompanying demonstrations and assignments. | |||||
Inhalt | 1) Introductory Concepts Matrices and linear algebra - short review. 2) The Direct Stiffness Method Demos and exercises in MATLAB or Python 3) Formulation of the Method of Finite Elements. - The Principle of Virtual Work - Isoparametric formulations - 1D Elements (truss, beam) - 2D Elements (plane stress/strain) Demos and exercises in MATLAB or Python 4) Practical application of the Method of Finite Elements. - Practical Considerations - Results Interpretation - Exercises, where structural case studies are modelled and analyzed | |||||
Skript | The lecture notes are in the form of slides, available online from the course webpage: Link | |||||
Literatur | Structural Analysis with the Finite Element Method: Linear Statics, Vol. 1 & Vol. 2 by Eugenio Onate (available online via the ETH Library) Supplemental Reading Bathe, K.J., Finite Element Procedures, Prentice Hall, 1996. | |||||
Voraussetzungen / Besonderes | Prior basic knowledge of Python is necessary. | |||||
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 (Link). | |||||
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. | |||||
101-0008-00L | Structural Identification and Health Monitoring | W | 3 KP | 2G | E. Chatzi, V. Ntertimanis | |
Kurzbeschreibung | This course will present methods for structural identification and health monitoring. We show how to exploit measurements of structural response (e.g. strains, deflections, accelerations) for evaluating structural condition, with the purpose of maintaining a safe and resilient infrastructure. | |||||
Lernziel | This course aims at providing a graduate level introduction into the identification and condition assessment of structural systems. Upon completion of the course, the students will be able to: 1. Test Structural Systems for assessing their condition, as this is expressed through measurements of dynamic response. 2. Analyse vibration signals for identifying characteristic structural properties, such as frequencies, mode shapes and damping, based on noisy measurements of the structural response. 3. Formulate structural equations in the time and frequency domain 4. Identify possible damage into the structure by picking up statistical changes in the structural behavior | |||||
Inhalt | The course will include theory and algorithms for system identification, programming assignments, as well as laboratory and field testing, thereby offering a well-rounded overview of the ways in which we may extract response data from structures. The topics to be covered are : 1. Elements of Vibration Theory 2. Transform Domain Methods 3. Digital Signals (P 4. Nonparametric Identification for processing test and measurement data (transient, correlation, spectral analysis) 5. Parametric Identification (time series analysis, transfer functions) A series of computer/lab exercises and in-class demonstrations will take place, providing a "hands-on" feel for the course topics. Grading: - This course offers optional homework as learning tasks, which can improve the grade of the end-of-semester examination up to 0.25 grade points (bonus). - The learning tasks will be taken into account if all 3 homeworks are submitted. The maximum grade of 6 can also be achieved by sitting the final examination only. | |||||
Skript | The course script is composed by the lecture slides, which are available online and will be continuously updated throughout the duration of the course: Link | |||||
Literatur | Suggested Reading: T. Söderström and P. Stoica: System Identification, Prentice Hall International: Link | |||||
Voraussetzungen / Besonderes | Familiarity with MATLAB is advised. | |||||
102-0488-00L | Water Resources Management | W | 3 KP | 2G | A. Castelletti | |
Kurzbeschreibung | Modern engineering approach to problems of sustainable water resources, planning and management of water allocation requires the understanding of modelling techniques that allow to account for comprehensive water uses (thereby including ecological needs) and stakeholders needs, long-term analysis and optimization. The course presents the most relevant approaches to address these problems. | |||||
Lernziel | The course provides the essential knowledge and tools of water resources planning and management. Core of the course are the concepts of data analysis, simulation, optimization and reliability assessment in relation to water projects and sustainable water resources management. | |||||
Inhalt | The course is organized in four parts. Part 1 is a general introduction to the purposes and aims of sustainable water resources management, problem understanding and tools identification. Part 2 recalls Time Series Analysis and Linear Stochastic Models. An introduction to Nonlinear Time Series Analysis and related techniques will then be made in order to broaden the vision of how determinism and stochasticity might sign hydrological and geophysical variables. Part 3 deals with the optimal allocation of water resources and introduces to several tools traditionally used in WRM, such as linear and dynamic programming. Special attention will be devoted to optimization (deterministic and stochastic) and compared to simulation techniques as design methods for allocation of water resources in complex and competitive systems, with focus on sustainability and stakeholders needs. Part 4 will introduce to basic indexes used in economical and reliability analyses, and will focus on multicriteria analysis methods as a tool to assess the reliability of water systems in relation to design alternatives. | |||||
Skript | A copy of the lecture handouts will be available on the webpage of the course. Complementary documentation in the form of scientific and technical articles, as well as excerpts from books will be also made available. | |||||
Literatur | A number of book chapters and paper articles will be listed and suggested to read. They will also be part of discussion during the oral examination. | |||||
Voraussetzungen / Besonderes | Suggested relevant courses: Hydrologie I (or a similar content course) and Wasserhaushalt (Teil "Wasserwirtschaft", 4. Sem. UmweltIng., or a similar content course) for those students not belonging to Environmental Engineering. | |||||
101-0269-00L | River Morphodynamic Modelling | W | 3 KP | 2G | D. F. Vetsch, D. Vanzo | |
Kurzbeschreibung | The course teaches the basics of morphodynamic modelling, relevant for civil and environmental engineers. The governing equations for sediment transport in open channels and corresponding numerical solution strategies are introduced. The theoretical parts are discussed by examples. | |||||
Lernziel | The goal of the course is twofold. First, the students develop a throughout understanding of the basics of river morphodynamic processes. Second, they get familiar with numerical tools for the simulations in one- and two-dimensions of morphodynamics. | |||||
Inhalt | - fundamentals of river morphodynamics (Exner equation, bed-load, suspended-load) - aggradation and degradation processes - river bars - non-uniform sediment morphodynamics: the Hirano model - short and long term response of gravel bed rivers to change in sediment supply | |||||
Skript | Lecture notes, slides shown in the lecture and software can be downloaded | |||||
Literatur | Citations will be given in lecture. | |||||
Voraussetzungen / Besonderes | Exercises are based on the simulation software BASEMENT (Link), the open-source GIS Qgis (Link) and code examples written in MATLAB and Python. The applications comprise one- and two-dimensional approaches for the modelling of flow and sediment transport. Requirements: Numerical Hydraulics, River Engineering, MATLAB and/or Python programming skills would be an advantage. | |||||
101-0368-00L | Constitutive and Numerical Modelling in Geotechnics The priority is given to the students with Major in Geotechnics. It uses computer room with a limited number of computers and software licenses. | W | 6 KP | 4G | A. Puzrin, D. Hauswirth | |
Kurzbeschreibung | This course aims to achieve a basic understanding of conventional continuum mechanics approaches to constitutive and numerical modeling of soils in getechnical problems. We focus on applications of the constitutive models within the available numerical codes. Important issue of derivation of model parameters from the lab tests has also received considerable attention. | |||||
Lernziel | This course targets geotechnical engineers, who face these days more often the necessity of the numerical analysis in their practice. Understanding of the limitations of the built-in constitutive models is crucial for critical assessment of the results of numerical calculations, and, hence, for the conservative and cost efficient design of geotechnical structures. The purpose of this course has been to bridge the gap between the graduate courses in Geomechanics and those in Numerical Modeling. Traditionally, in many geotechnical programs, Geomechanics is not taught within the rigorous context of Continuum Mechanics. There is a good reason for that – the behavior of soils is very complex: it is more advantageous to explain it at a semi-empirical level, instead of scaring the students away with cumbersome mathematical models. However, when it comes to Numerical Modeling courses, these are often taught using commercially available finite elements (e.g. ABAQUS, PLAXIS) or finite differences (e.g. FLAC) software, which utilize constitutive relationships within the Continuous Mechanics framework. Quite often students have to learn the challenging subject of constitutive modeling from a program manual! | |||||
Inhalt | This course is introductory - by no means does it claim any completeness and state of the art in such a dynamically developing field as constitutive and numerical modeling of soils. Our intention is to achieve a basic understanding of conventional continuum mechanics approaches to constitutive and numerical modeling, which can serve as a foundation for exploring more advanced theories. We focus on applications of the constitutive models within the available numerical codes. Important issue of derivation of model parameters from the lab tests has also received considerable attention. | |||||
Skript | Handout notes Example worksheets | |||||
Literatur | - Puzrin, A.M. (2012). Constitutive Modelling in Geomechanics: Introduction. Springer Verlag. Heidelberg, 312 p. | |||||
101-0378-00L | Soil Dynamics | W | 3 KP | 2G | I. Anastasopoulos, A. Marin, T. M. Weber | |
Kurzbeschreibung | Grundlagen bodendynamischer Problemstellungen, Einführung in das geotechnische Erdbebeningenieurwesen, Lösen einfacher Probleme | |||||
Lernziel | Vermittlung der Grundlagen, um bodendynamische Problemstellungen erkennen zu können, einfache Probleme selbständig zu lösen und bei komplexeren Aufgaben Spezialisten effizient beauftragen zu können. | |||||
Inhalt | Grundlagen der Dynamik und der Bodendynamik: Unterschiede und Gemeinsamkeiten Bodenmechanik-Bodendynamik. Repetition der Grundlagen am Beispiel des Einmassenschwingers; Wellenausbreitung im elastischen Halbraum und im realen Boden. Einfluss der geologischen Schichtung, des Grundwassers etc. auf Wellenausbreitung. Dynamische Bodenkennziffern (Deformation und Festigkeit): Konstitutive Modellierung des Bodens, Bodenkennziffern für Sand, Kies, Ton, Fels. Bestimmung der Bodenkennziffern im Labor und Feld. Erschütterungen: Ausbreitungsprognose von Erschütterungen. Beurteilung von Erschütterungen bezüglich Gebäudeschäden und Belästigung des Menschen. Reduktion von Erschütterungen. Geotechnische Erdbebenprobleme: Grundbegriffe. Schäden infolge Erdbeben. Analyse der seismischen Gefährdung, Ermittlung von Bemessungsbeben. Einfluss der lokalen Geologie und Topographie auf die Bodenerschütterung. Grundlagen der Boden-Bauwerksinteraktion. Grundsätze der erdbebengerechten Dimensionierung von Fundationen, Stütz- und Erdbauwerken (Dämme). Bodenverflüssigung. Anwendung der SIA 261/267/269-8. Probleme der Gebrauchstauglichkeit: Bleibende Verformungen aufgrund wiederholter Belastung, Sackungen | |||||
Skript | Buch Studer, J.; Laue, J. & Koller, M.: Bodendynamik, Springer Verlag 2007 Ergänzt durch Aufsätze und Notizen die elektronisch zu Verfügung gestellt werden | |||||
Literatur | Towhata, I. (2008) Geotechnical Earthquake Engineering. Springer Verlag, Berlin Kramer, S. L. (1996) Geotechnical earthquake engineering. Pearson Education India. | |||||
Voraussetzungen / Besonderes | Voraussetzungen: Grundlagenwissen der Mechanik und der Geotechnik | |||||
101-0526-00L | Introduction to Visual Machine Perception for Architecture, Construction and Facility Management | W | 3 KP | 2G | I. Armeni | |
Kurzbeschreibung | The course is an introduction to Visual Machine Perception technology, and specifically Computer Vision and Machine Learning, for Architecture, Construction, and Facility Management (ACFM). It will explore fundamentals in these Artificial Intelligence (AI) technologies in a tight reference to three applications in ACFM, namely architectural design, construction renovation, and facility management. | |||||
Lernziel | By the end of the course students will develop computational thinking related to visual machine perception applications for the ACFM domain. Specifically, they will: -Gain a fundamental understanding of how this technology works and the impact it can have in the ACFM industry by being exposed to example applications. -Be able to identify limitations, pitfalls, and bottlenecks in these applications. -Critically think on solutions for the above issues. -Acquire hands-on experience in creatively thinking and designing an application given a base system. -Use this course as a “stepping-stone” or entry-point to Machine Learning-intensive courses offered in D-BAUG and D-ARCH. | |||||
Inhalt | The past few years a lot of discussion has been sparked on AI in the Architecture, Construction, and Facility Management (ACFM) industry. Despite advancements in this interdisciplinary field, we still have not answered fundamental questions about adopting and adapting AI technology for ACFM. In order to achieve this, we need to be equipped with rudimentary knowledge of how this technology works and what are essential points to consider when applying AI to this specific domain. In addition, the availability of sensors that collect visual data in commodity hardware (e.g., mobile phone and tablet), is creating an even bigger pressure in identifying ways that new technology can be leveraged to increase efficiency and decrease risk in this trillion-dollar industry. However, cautious and well-thought steps need to be taken in the right direction, in order for such technologies to thrive in an industry that showcases inertia in technological adoption. The course will unfold as two parallel storylines that intersect in multiple places: 1) The first storyline will introduce fundamentals in computer vision and machine learning technology, as building blocks that one should consider when developing related applications. These blocks will be discussed with respect to latest developments (e.g., deep neural networks), pointing out their impact in the final solution. 2) The second storyline consists of 3 ACFM processes, namely architectural design, construction renovation, and facility management. These processes will serve as application examples of the technological storyline. In the points of connection students will see the importance of taking into account the application requirements when designing an AI system, as well as their impact on the building blocks. Guest speakers from both the AI and ACFM domains will complement the lectures. | |||||
Voraussetzungen / Besonderes | The course does not require any background in AI, Computer Science, coding, or the ACFM domain. It is designed for students of any background and knowledge on these topics. Despite being an introductory class, it will still engage advanced students in the aforementioned topics. | |||||
101-0185-01L | CAD für Bauingenieure Maximale Teilnehmerzahl: 30 pro Kurs. Es zählt der Zeitpunkt der Einschreibung. | W | 2 KP | 2G | K.‑H. Hamel, F. Ortiz Quintana | |
Kurzbeschreibung | Einführung in das Arbeiten mit CAD-Software. Anfertigung bautechnischer Zeichnungen in 2D und 3D. | |||||
Lernziel | Nach Abschluss des Kurses können die Absolventen eine 2D-Konstruktion erstellen (Schalungsplan) und sie kennen das Prinzip eines Bewehrungsmoduls. Ferner haben sie eine Einführung in ein 3D-Programm enthalten (3D-Bewehren). Sie sind somit besser vorbereitet auf - die Bachelorarbeit im 6. Semester, - ein allfälliges Praktikum zwischen Bachelor- und Masterstudium, - die Projektarbeiten im Masterstudium, - die Masterarbeit. Ausserdem schulen sie das räumliche Vorstellungsvermögen und erwerben sich Orientierungswissen als spätere Vorgesetzte von Zeichnern und Konstrukteuren. | |||||
Inhalt | Vermassung. Erzeugung von Schnitten und Ansichten. Anwendung des Bewehrungsmoduls. Erstellung abgabefertiger Pläne. | |||||
Skript | Autographie | |||||
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. |
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