Paid master thesis: Integration of machine learning methods into numerical simulation
Field of study: Mathematics, Physics, Informatics, Telematics
or similar scientif ic subjects
The combination of common physics-based methods with data-driven models to so-called hybrid approaches is a current
topic in the field of numerical simulation. Since hybrid models show great potential to increase prediction accuracy
compared to standard data-driven methods and/or accelerate physics-based numerical simulation, the idea of this approach
is to combine the benefits of both worlds.
In the frame of this Master thesis, a novel method which directly integrates machine learning methods into the numerical
scheme used for solving partial differential equations shall be elaborated. Therefore, an existing baseline concept shall be
further developed and its application to more complex physical problems shall be investigated. Paid master thesis
or similar scientif ic subjects
The combination of common physics-based methods with data-driven models to so-called hybrid approaches is a current
topic in the field of numerical simulation. Since hybrid models show great potential to increase prediction accuracy
compared to standard data-driven methods and/or accelerate physics-based numerical simulation, the idea of this approach
is to combine the benefits of both worlds.
In the frame of this Master thesis, a novel method which directly integrates machine learning methods into the numerical
scheme used for solving partial differential equations shall be elaborated. Therefore, an existing baseline concept shall be
further developed and its application to more complex physical problems shall be investigated. Paid master thesis
The LEC supports equal opportunity and diversity. We are looking for committed and motivated individuals with a talent for research. Become part of our successful LEC team! We look forward to your application.
We are looking forward to your application!
Contact:
Herlinde Kohlmaier
Email: career@lec.tugraz.at