Akselos provides Digital Twins of industrial equipment in a range of industries, such energy, renewables, mining, and aerospace. The Akselos platform is based on RB-FEA, which is a unique combination of the Reduced Basis method for fast reduced order modeling of parametrized PDEs, with a domain decomposition framework that enables large-scale component-based analysis. RB-FEA shares many similarities to supervised learning approaches, in which "full order" solutions are used as the "supervisor" during RB-FEA training. In this presentation we will discuss the similarities and differences between RB-FEA and other ML methods, and demonstrate applications of the methodology to industrial Digital Twins.