LBRG Optimal Control and Optimization Lab

LBRG
Resolved simulation of deformable red blood cell membranes with OpenLB

The LBRG Optimal Control and Optimization Lab explores the intersection of lattice Boltzmann methods (LBM) and optimization techniques, with a strong focus on applications in process and biomedical engineering. Our research tasks spans from technical topics such as efficient algorithms for gradient computation and automated code generation of adjoint operators, to the mathematical development of new optimization methods and their theoretical foundations. Beyond methodology, we address challenging real-world problems, including inverse problems and topology optimization for complex engineering and biomedical systems. The inherently interdisciplinary nature of our work, combining numerical methods, optimization, and application-driven research, is a defining characteristic of the lab.

Lab Lead
Title First Name Surname Phone E-Mail
Shota Ito, M. Sc.   shota ito ∂does-not-exist.kit edu
Lab Members
Title First Name Surname Phone E-Mail
Johannes Leonard Grafen, M.Sc. +49 721 608-44138 johannes grafen ∂does-not-exist.kit edu

Selected Publications

  • S. Ito, J. Jeßberger, S. Simonis, F. Bukreev, A. Kummerländer, A. Zimmermann, G. Thäter, G. R. Pesch, J. Thöming, and M. J. Krause. Identification of reaction rate parameters from uncertain spatially distributed concentration data using gradient-based pde constrained optimization. In: Computers & Mathematics with Applications (2024). DOI: https://doi.org/10.1016/j.camwa.2024.05.026.
  • S. Ito, S. Großmann, F. Bukreev, J. Jeßberger, and M. J. Krause Benchmark case for the inverse determination of adsorption parameters using lattice boltzmann methods and gradient-based optimization. In: Chemical Engineering Science (2025). DOI: https://doi.org/10.1016/j.ces.2025.121467.
  • S. Ito, A. Zimmermann, J. Jeßberger, S. Simonis, A. Kummerländer, F. Bukreev, J. Thöming, G. Pesch, M.J. Krause. Geometry Reconstruction from Magnetic Resonance Velocimetry Measurements via Solving an Inverse Fluid Flow Problem. Preprint, under review. DOI: https://ssrn.com/abstract=5313346.
  • A. Kummerländer, F. Bukreev, Y. Shimojima, S. Ito, and M. J. Krause. Large-scale simulations of turbulent flows using lattice Boltzmann methods on heterogeneous high performance computers. In: Preprint, under review. DOI: https://arxiv.org/abs/2506.21804.
  • F. Klemens, B. Förster, M. Dorn, G. Thäter, and M. J. Krause. "Solving fluid flow domain identification problems with adjoint lattice Boltzmann methods." Computers & Mathematics with Applications (2020). DOI: https://doi.org/10.1016/j.camwa.2018.07.010.
  • F. Klemens, S. Schuhmann, R. Balbierer, G. Guthausen, H. Nirschl, G. Thäter, and M. J. Krause. "Noise reduction of flow MRI measurements using a lattice Boltzmann based topology optimisation approach". Computers & Fluids (2020). DOI: https://doi.org/10.1016/j.compfluid.2019.104391.
  • J. Jeßberger, J. E. Marquardt, L. Heim, J. Mangold, F. Bukreev, and M. J. Krause. "Optimization of a micromixer with automatic differentiation". Fluids (2022). DOI: https://doi.org/10.3390/fluids7050144.
  • M. Rüttgers, M. Waldmann, S. Ito, C. Wüstenhagen, S. Grundmann, M. Brede, and A. Lintermann. Patient-specific lattice-Boltzmann simulations with inflow conditions from magnetic resonance velocimetry measurements for analyzing cerebral aneurysms. In: Computers in Biology and Medicine (2025). DOI: https://doi.org/10.1016/j.compbiomed.2025.109794.