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In recent years, numerical methods in industrial applications have evolvedfrom a pure predictive tool towards a means for optimization and control. Sincestandard numerical analysis methods have become prohibitively costly in suchmulti-query settings, a variety of reduced order modeling (ROM) approaches havebeen advanced towards complex applications. In this context, the drivingapplication for this work is twin-screw extruders (TSEs): manufacturing deviceswith an important economic role in plastics processing. Modeling the flowthrough a TSE requires non-linear material models and coupling with the heatequation alongside intricate mesh deformations, which is a comparativelycomplex scenario. We investigate how a non-intrusive, data-driven ROM can beconstructed for this application. We focus on the well-established properorthogonal decomposition (POD) with regression albeit we introduce twoadaptations: standardizing both the data and the error measures as well as --inspired by our space-time simulations -- treating time as a discretecoordinate rather than a continuous parameter. We show that these steps makethe POD-regression framework more interpretable, computationally efficient, andproblem-independent. We proceed to compare the performance of three differentregression models: Radial basis function (RBF) regression, Gaussian processregression (GPR), and artificial neural networks (ANNs). We find that GPRoffers several advantages over an ANN, constituting a viable andcomputationally inexpensive non-intrusive ROM. Additionally, the framework isopen-sourced to serve as a starting point for other practitioners andfacilitate the use of ROM in general engineering workflows.

Standardized Non-Intrusive Reduced Order Modeling Using Different Regression Models With Application to Complex Flow Problems