Before revision: We present a non-intrusive reduced basis method (RBM) for unsteady non-linear parametrized partial differential equations (PDEs)based on proper orthogonal decomposition (POD) with regression . Three different regression models are compared, including radial basis function (RBF) regression, Gaussian process regression (GPR)  and artificial neural networks (ANNs).
After revision: We present a non-intrusive reduced basis method (RBM) for unsteady non-linear parametrized partial differential equations (PDEs)based on proper orthogonal decomposition (POD) with regression albeit we introduce two adaptations: standardizing both the data and the error measures as well as -- inspired by our space-time simulations -- treating time as a discrete coordinate rather than a continuous parameter. We show that these steps make the POD-regression framework more interpretable, computationally efficient, and problem-independent. We proceed to compare the performance of three different regression models are compared, including radial basis function (RBF) regression, Gaussian process regression (GPR) and artificial neural networks (ANNs).