Maximilian Reissmann
- reissmannm@student.unimelb.edu.au
- Desk 4.045, Level 4, Melbourne Connect
Thesis Title
Development of a neural hybrid symbolic regression algorithm for the development of more general and robust turbulence models
Research overview
My PhD-research project focuses on the optimization of data-driven learning algorithms to create more general turbulence models by trying to reduce computational costs for the exploration. One aspect of this research is to hybridize different approaches for symbolic regression to synthesize the respective advantages and potentially use them to predict more robust and accurate models.
Supervisors
Qualifications
M.Sc. Computer Science, University of the Federal Armed Forces Munich, Germany (2019)
B.Sc. Computer Science, University of the Federal Armed Forces Munich, Germany (2018)