Multi-Objective Optimization of Aerodynamic Shapes by an Approach Based on Reinforcement Learning
The project aims to develop an innovative methodology for the optimization of aerodynamic shapes using advanced reinforcement learning techniques. The aim is to develop robust and efficient optimization methods that can be applied to a range of complex problems in aerospace engineering, including the design of wing profiles, air vehicle bodies and turbomachinery components.
The successful candidate will work on both the theoretical development of the optimization approach based on reinforcement learning and its practical implementation through numerical simulations. Collaborations with industrial partners may be considered in order to validate the results obtained and explore concrete applications in the field of aeronautics.
Required knowledge
- Bachelor's degree in software engineering, mechanical engineering, aerospace engineering, or a related field.
- Programming skills, preferably in Python, as well as familiarity with machine learning libraries such as TensorFlow, PyTorch or scikit-learn.
- Prior experience with machine learning and reinforcement learning techniques and a good understanding of aerodynamics and optimization concepts would be an asset.
- Demonstrated ability to carry out research projects independently and work in a team.
- Previous experience in numerical modeling and simulation will be appreciated.