WHAT : In this course various (astro)physical problems will be solved with diverse computational methods: Fourier-transformation, population synthesis & Markov chain Monte Carlo, N-body simulations, Hydrodynamical/Computational fluid dynamics simulations, High Performance Computing, radiative transfer, advanced visualization techniques. We review the various computational methods used in (astro)physics, with a problem-oriented approach: we take an astrophysical problem and discuss how to solve that type of problem numerically. We will do data analysis, computer simulations, and visualization approaches that are not only used in astrophysics, but other physical fields, mathematical fields and engineering. Content: Prerequisites: basic linux terminal commands, basic programming knowledge in any language. Further information can be found in the course catalogue. May 2-6 & May 9-10, 2022 in the afternoon, online via Zoom Max. 20 participants Please be aware that the registration opens January 1 with a deadline at the end of the 2nd week of the Spring Semester 2022. Registration Conditions WHAT : 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. This 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. The topics to be covered are in three broad categories, with a detailed outline available online (see Learning Materials). The lectures will be complemented via a comprehensive series of interactive Tutorials. Prerequisites: Introductory course on probability theory and fair command on Matlab Further information can be found in the course catalogue. May 9-20, 2022 from 9.00-17.00 h (whereof May 12-16 will be entirely virtual) Please be aware that the registration opens January 1 with a deadline at the end of the 2nd week of the Spring Semester 2022. Registration Conditions
Lecturers: Prof. Dr. Bruno Sudret, Dr. Stefano Marelli
Lecturers: Prof. Dr. Costas Papadimitriou, Dr. Georgios Arampatzis, Prof. Dr. Petros Koumoutsakos
Lecturers: Prof. Dr. Eleni Chatzi, Dr. Vasilis Dertimanis
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