Compact dynamical modeling

Numerical methods for the generation of compact (i.e., small and efficient) models of complex linear and nonlinear dynamical systems. Methods include both data-driven algorithms for the extraction of models from available responses (measured or obtained through first-principle field solvers), as well as model-driven algorithms aiming at reducing the size (number of states) of large-scale or detailed physics-based models (which may be too complex to simulate in reasonable runtime). Stochastic formulations are also pursued for uncertainty quantification. Main tools are: model order reduction, system identification via rational based approximation and time- or frequency-domain data, surrogate modeling tools for linear and nonlinear systems, polynomial chaos expansions. This research activity is strongly multidisciplinary and may provide useful contributions to a large variety of research and application areas.

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ERC Sector:

  • PE7_3 Simulation engineering and modelling
  • PE7_4 (Micro and nano) systems engineering
  • PE7_11 Components and systems for applications

Keywords:

  • Reduced order systems
  • System identification
  • Surrogate modeling
  • Stochastic modeling and simulation

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