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| MECA0010-1 | Stochastic modelling
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| Duration : | 30h Th, 30h Pr |
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| Number of credits : |
| Master in Aerospace Engineering, research focus, 2nd year | | 5 |
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| Master in Biomedical Engineering, research focus, 2nd year | | 5 |
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| Master in Mechanical Engineering, research focus, 1st year | | 5 |
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| Master in Engineering Physics, research focus, 2nd year | | 5 |
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| Master in Mechanical Engineering, professional focus in sustainable car technologies, 1st year | | 5 |
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| Master in Mechanical Engineering, specialized approach, 1st year | | 5 |
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| Lecturer : | Maarten Arnst |
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Language(s) of instruction :
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| English language |
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Organisation and examination :
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| Teaching in the second semester |
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Course contents :
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| Owing to various experimental and modeling limitations, many properties and actions of physical systems can be subjected to uncertainty; hence, the behavior of these systems cannot always be predicted or controlled precisely. Such uncertainty can manifest itself in various ways in applications, for example, as a limited predictability horizon in weather forecasting, as tolerances in manufacturing processes, or as noise in mobile communication. Based on probability theory, the field of stochastic modeling provides a framework for quantifying and managing uncertainties in applications in science and engineering.
This course offers an introduction to stochastic modeling and uncertainty quantification. First, the course introduces conceptual, mathematical, and computational aspects of the stochastic modeling of mechanical and physical systems in the presence of uncertainty. Then, through a class project, the course will seek to highlight how stochastic modeling can help address challenges posed by uncertainty in numerical modeling, design, and other engineering processes. |
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Learning outcomes of the course :
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| At the end of the course, students will be able to identify sources of uncertainty that may affect engineering systems, as well as to put in place techniques for accounting for uncertainties in numerical modeling, design, and other engineering processes. |
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Prerequisites and co-requisites/ Recommended optional programme components :
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| This course assumes that students have a background in probability theory (MATH0062 "Elements of probability calculus"), statistics (MATH0487 "Elements of statistics"), and stochastic processes (MATH0488 "Elements of stochastic processes"). The required background material will be recalled in class as needed. |
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Planned learning activities and teaching methods :
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| The course takes the form of a series of lectures. In addition, students work in small groups on a class project. |
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Mode of delivery (face-to-face ; distance-learning) :
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| Face-to-face. |
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Recommended or required readings :
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| Each lecture is supported by slides prepared by the instructor. Papers from the historic and current international scientific literature complement the slides. |
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Assessment methods and criteria :
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| Students are required to prepare a classroom presentation of their class project and document their work in a concise report. The final grade is an equally weighted average of the grades obtained for the classroom presentation and the report, which takes into account their content, clarity, and neatness. |
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Work placement(s) :
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Organizational remarks :
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| The course will be offered in the Spring semester. |
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Contacts :
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| Maarten Arnst
Bureau: B52 - 0/419
Email: Maarten.Arnst@ulg.ac.be |
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