  A surrogate model approximates the outputs of a solver of Partial
Differential Equations (PDEs) with a low computational cost. In this article,
we propose a method to build learning-based surrogates in the context of
parameterized PDEs, which are PDEs that depend on a set of parameters but are
also temporal and spatial processes. Our contribution is a method hybridizing
the Proper Orthogonal Decomposition and several Support Vector Regression
machines. This method is conceived to work in real-time, thus aimed for being
used in the context of digital twins, where a user can perform an interactive
analysis of results based on the proposed surrogate. We present promising
results on two use cases concerning electrical machines. These use cases are
not toy examples but are produced an industrial computational code, they use
meshes representing non-trivial geometries and contain non-linearities.
