0730 Structure-preserving model reduction for nonlinear systems

  • Boris Kramer, University Of California San Diego
  • Yuto Miyatake

Nonlinear dynamical systems abound in engineering and science, and their long-term simulation and outer-loop applications such as control, design and uncertainty quantification remains a computational challenge. From first principles modeling, it is clear that many of these systems have a natural mechanical structure (e.g., Hamiltonian, Lagrangian). Exploiting this structure in spatial and time approximation, as well as in model reduction remains imperative for certifiable reduced-order modeling. This minisymposium highlights recent developments in model reduction for nonlinear structured systems, such as: port-Hamiltonian model reduction, geometric model reduction, passivity-based methods, preservation of conservation laws, the incorporation of interconnection and modular structure, structure preserving data based realization and learning approaches.

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