13763 - Flow Modeling with Machine Learning Modulübersicht
Module Number: | 13763 |
Module Title: | Flow Modeling with Machine Learning |
Strömungsmodellierung anhand maschinelles Lernen | |
Department: | Faculty 3 - Mechanical Engineering, Electrical and Energy Systems |
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Language of Teaching / Examination: | English |
Duration: | 1 semester |
Frequency of Offer: | Every summer semester |
Credits: | 6 |
Learning Outcome: | The students are offered an introduction to machine learning in the context of computational fluid dynamics, and turbulent flow analysis and modeling. Elementary definitions and concepts in machine learning will be motivated by CFD applications. A large part of the course is dedicated to the analysis of numerical simulation data using supervised learning approaches. Some aspects of flow feature extraction using unsupervised learning, reduced order modeling, as well as general State-Of-The-Art research issues in machine learning for CFD will also be discussed. At the end of the course, the students are able to understand the key concepts and algorithms in machine learning and how they could be applied in CFD. |
Contents: | The course contents offer an overview on key machine learning concepts and fundamentals, and how they can be applied in CFD, mainly in the context of analysis of numerical simulations, and modeling of turbulent flows.
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Recommended Prerequisites: | The student should be highly motivated to study numerical simulations of fluid flows using computational methods. This is a Python-based course demanding some relevant programming background. To that extent, successful completion of the module “Introduction to Computational Thinking and Programming for CFD” is highly desired (but not mandatory). Completion of the module “Turbulence Modeling” is also recommended for a better theoretical overview, but not mandatory. |
Mandatory Prerequisites: | None |
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Module Examination: | Prerequisite + Final Module Examination (MAP) |
Assessment Mode for Module Examination: | Prerequisite:
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Evaluation of Module Examination: | Performance Verification – graded |
Limited Number of Participants: | None |
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Remarks: | The module primarily aims at Master students in the engineering and natural sciences who plan to specialize in computational fluid dynamics. |
Module Components: | . |
Components to be offered in the Current Semester: |