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Model Order Reduction is a key technology for industrial applications in thecontext of digital twins. Key requirements are non-intrusiveness,physics-awareness, as well as robustness and usability. Operator inferencebased on least-squares minimization combined with the Discrete EmpiricalInterpolation Method captures most of these requirements, though the requiredregularization limits usability. Within this contribution we reformulate theproblem of operator inference as a constrained optimization problem allowing torelax on the required regularization. The result is a robust model orderreduction approach for real-world industrial applications, which is validatedalong a dynamics complex 3D cooling process of a multi-tubular reactor using acommercial software package.

A Differentiable Solver Approach to Operator Inference