ATML Numerical and Statistical Methods

The ATML "Numerical and Statistical Methods" develops, implements, and tests new procedures/methods in the area of stochastic ordinary and partial differential equations that arise, for example, in image processing, acoustic, electromagnetic, and elastic scattering problems, and reaction-diffusion-advection equations.
In this context, the group also deals with algorithms for the solution of ill-posed inverse problems and the computation of eigenvalue problems.

Furthermore, the group offers software support for supercomputers.

Contact: Prof. Dr. Andreas Kleefeld

Special Methods for PDEs

Partial differential equations (PDEs) in unbounded domains can be solved efficiently with boundary integral equations.

Parallel FEM Simulations for Large Infrastructures

Another activity concerning PDEs is the analysis of complex infrastructures, like a long-span suspension bridge crossing the Bosphorus.

Parallel Preconditioning via Reinforcement Learning

Machine learning can be used to improve existng mathematical algorithms which approximate a PDE solution.