  AI integration is revolutionizing the landscape of HPC simulations, enhancing
the importance, use, and performance of AI-driven HPC workflows. This paper
surveys the diverse and rapidly evolving field of AI-driven HPC and provides a
common conceptual basis for understanding AI-driven HPC workflows.
Specifically, we use insights from different modes of coupling AI into HPC
workflows to propose six execution motifs most commonly found in scientific
applications. The proposed set of execution motifs is by definition incomplete
and evolving. However, they allow us to analyze the primary performance
challenges underpinning AI-driven HPC workflows. We close with a listing of
open challenges, research issues, and suggested areas of investigation
including the the need for specific benchmarks that will help evaluate and
improve the execution of AI-driven HPC workflows.
