  Model Order Reduction is a key technology for industrial applications in the
context of digital twins. Key requirements are non-intrusiveness,
physics-awareness, as well as robustness and usability. Operator inference
based on least-squares minimization combined with the Discrete Empirical
Interpolation Method captures most of these requirements, though the required
regularization limits usability. Within this contribution we reformulate the
problem of operator inference as a constrained optimization problem allowing to
relax on the required regularization. The result is a robust model order
reduction approach for real-world industrial applications, which is validated
along a dynamics complex 3D cooling process of a multi-tubular reactor using a
commercial software package.
