Speaker
Description
Operator inference (OpInf) is a method for non-intrusive reduced modeling, i.e., identifying small surrogate models from data. In this poster, we consider second-order ODE systems, which are typically used to model mechanical vibrations. The variety of load cases requires specific adaptations of the OpInf method, taking into account different approaches to modeling the corresponding problems, as well as the characteristics of the output data. This poster shows extensions of the OpInf approach that allow us to preserve the structure of the system matrices, and thus the physical meaning and properties of the full-order system. Depending on the problem, the presented OpInf methods use semidefinite programming or parameterization of the unknown coefficients for structure preservation. In addition, a novel non-intrusive procedure for contact problems is presented that combines OpInf with classical intrusive substructuring approaches.
We present applications of the OpInf method to simple bending and rotating structures, such as beams and rotors, as well as to more complex systems, such as contact problems with a rigid obstacle. Using only data that stem from simplified simulations, the OpInf method allows efficient construction of reduced-order models, providing interpretable and meaningful results even for dynamic cases not included in the training data.
