ADMOS 2023

MS02 - Adaptive Methods for Surrogate and Reduced Order Modeling

R. Janicke (TU Braunschweig, Germany), U. Römer (TU Braunschweig, Germany) and C. Gräßle (TU Braunschweig, Germany)
Adaptivity is a key enabler towards simulating complex problems, for example multifield, nonlinear and multiscale problems. Model order reduction and surrogate modeling are nowadays indispensable tools that enable an efficient simulation. The minisymposium welcomes contributions which address problems in engineering applications and feature adaptive approaches to surrogate modeling, reduced order modeling or a combination of both. Examples are

  • reduced order modeling, reduced FE2
  • high-dimensional surrogate modeling
  • surrogate models for, e.g., regression of local ROMs, response surfaces with reduced regularity,...
  • data-driven and hybrid approaches which are combined with error-controlled or heuristic adaptivity. A large range of applications is welcome.