Scaling GP-based PDE Solvers

Research Project at the Methods of Machine Learning Group, supervised by Marvin Pförtner.

The project explored techniques to scale probabilistic PDE solvers to larger, more complex problems. The animation above demonstrates the result of this work: A simulation of the 1D shallow-water equations. This was not tractable within the previous framework.

Don’t worry, this is not the end of the story. In my thesis, I developed techniques that scale substantially better…

Details (on this work and on future work) coming soon!

Tim Weiland
Tim Weiland
PhD student

Interested in fundamental machine learning research. I like math and software engineering and believe that both are crucial to build better algorithms.