Course paper / final thesis

Artificial Neural Networks for Data-Driven Aerodynamic Applications

Starting date


Duration of contract

6 months

Type of employment


"Cutting-edge research requires excellent minds – particularly more females – at all levels. Launch your mission with us and send in your application now!" Prof. Pascale Ehrenfreund - Chair of the DLR Executive Board

In the past few years the application of machine learning (ML) methods has increased rapidly throughout various domains including several engineering disciplines. In particular artificial neural networks (ANN) have been heavily investigated for reconstruction, clustering and classification problems. In aerodynamics, the main interest is in applying ML methods to predict different quantities of interest such as the lift coefficient or the surface pressure distributions at changing flight conditions based on just a few, well-chosen highly accurate and expensive simulations. The current approach to achieve such predictions is to combine dimensionality reduction methods and interpolation or regression techniques offering reasonable results for a range of aerodynamic problems. Nevertheless, a smart combination of established approaches with carefully selected ANN might yield another boost in accuracy and efficiency.

The emerging potential offered by ANN should be investigated during this master thesis and compared to existing and more established methods. Therefore the following steps could be seen as a rough guideline:

  • identification of potential artificial neural network architectures with respect to aerodynamic problems
  • selection of one or a few ANN approaches which seem promising for specific applications
  • demonstration of the performance of these architectures using simplified test cases
  • application to one or several aerodynamic test cases depending on previously drawn conclusions

Your qualifications:

  • student enrolled in information technologies, mathematics, aerospace engineering or similar fields
  • good knowledge of python
  • interest in AI methods in particular machine learning approaches
  • pre-knowledge of neural networks is a plus
  • pre-knowledge of aerodynamics is a plus

Your benefits:

Look forward to a fulfilling job with an employer who appreciates your commitment and supports your personal and professional development. Our unique infrastructure offers you a working environment in which you have unparalled scope to develop your creative ideas and accomplish your professional objectives. Our human resources policy places great value on a healthy family and work-life-balance as well as equal opportunities for persons of all genders (m/f/non-binary). Individuals with disabilities will be given preferential consideration in the event their qualifications are equivalent to those of other candidates.

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Technical contact

Dr. Philipp Bekemeyer
Institute of Aerodynamics and Flow Technology

Phone: +49 531 295-3871

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Vacancy 35639

HR department Braunschweig

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DLR site Braunschweig

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DLR Institute of Aerodynamics and Flow Technology

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