In 2004, Philip Colella presented “The Seven Dwarfs for Scientific Computing” wherein he outlined seven classes of algorithmic methods for scientific computation. Nearly two decades later, researchers from several academic and industrial institutions have proposed a new discipline, Simulation Intelligence, focused on applying AI and simulation science to scientific experimentation and discovery. Moreover, they’ve outlined nine new interconnected motifs associated with this discipline: multi-physics and multi-scale modeling; surrogate modeling and emulation; simulation-based inference; causal modeling and inference; agent-based modeling; probabilistic programming, differentiable programming; open-ended optimization; and machine programming. Here, they describe the layers of the Simulation Intelligence stack including the motifs associated with each layer.