Poster
in
Workshop: AI for Science: Scaling in AI for Scientific Discovery
A Fast Learning-Based Surrogate of Electrical Machines using a Reduced Basis
Alejandro Ribes · Nawfal BENCHEKROUN · Théo Delagnes
Keywords: [ POD ] [ Numerical Simulation ] [ SVR ] [ Machine-Learning for Science ] [ Surrogate Models ] [ partial differential equations ] [ Support Vector Regression ]
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 and are produced by an industrial computational code, they use meshes representing non-trivial geometries and contain non-linearities.