• Machine learning

Topics in detail

  • Machine learning and High-Dimensional Approximation
    • Inference / Data Assimilation and Uncertainty Quantification
    • Multi-Level/-Fidelity Techniques / Sparse Grids (Combination Technique)
    • Approximation in Reproducing Kernel Hilbert Spaces
    • Low-Rank Approximation (e.g. Hierarchical Matrices)
  • High Performance Computing and Scalable Algorithms
    • Optimal Complexity Algorithms
    • Algorithms for Many-Core Architectures (GPUs)
    • Scalability
  • Applications
    • Quantum Chemistry
    • Medical Imaging
    • Computational Fluid Dynamics (two-phase flows, plasma physics)


hmglib: hierarchical matrices on graphic processing units (github)

MPLA: multi-GPU parallel library for dense iterative matrix solvers (github)

Multi-GPU support und uncertainty quantification for two-phase Navier Stokes (NaSt3DGPF)