[ref. a55385718] Thesis -Application of PINNs on 3D CFD Data - Graz
We offer the following reserach topic:
Thesis -Application of PINNs on 3D CFD Data
Your Responsibilities
This thesis investigates the potential of Physics-Informed Neural Networks (PINNs) in the field of fluid dynamics. The focus is set on turbulence modelling allowing applications relevant to industrial contexts. The thesis should cover a comprehensive literature review, highlighting two promising use cases: (a) up-sampling and (b) acceleration of simulations.Up-sampling refers to enhancing the resolution of simulation outputs - such as velocity fields - from coarse to fine grids (also known as super-resolution). Acceleration encompasses methods aimed at reducing computational time for solving fluid dynamics problems.
Based on insights gained from the literature, one promising approach will be applied in a case study using a fluid dynamic dataset provided by AVL. The implementation will be carried out in Python, leveraging deep learning frameworks such as TensorFlow and PyTorch to incorporate state-of-the-art functionalities.
- Literature Review on PINNs
- Application of PINNs on 3D fluid dynamics data
- Ongoing studies in the fields of Mechanical Engineering, Data Science, Informatics or similar
- Experience in python coding
- Experience in Data Science beneficial
- Experience in Fluid Dynamics beneficial
- You can write your thesis independently and receive professional guidance and support from our experienced employees.
- You will have the opportunity to exchange ideas with experts in the company and benefit from their expertise.
- Take the opportunity to immerse yourself in the world of AVL and embed your theoretical knowledge in a practical environment.
The successful completion of the thesis is remunerated with a one-time fee of 3.500,00 EUR before tax.
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