  Increasingly, scientific discovery requires sophisticated and scalable
workflows. Workflows have become the ``new applications,'' wherein multi-scale
computing campaigns comprise multiple and heterogeneous executable tasks. In
particular, the introduction of AI/ML models into the traditional HPC workflows
has been an enabler of highly accurate modeling, typically reducing
computational needs compared to traditional methods. This chapter discusses
various modes of integrating AI/ML models to HPC computations, resulting in
diverse types of AI-coupled HPC workflows. The increasing need of coupling
AI/ML and HPC across scientific domains is motivated, and then exemplified by a
number of production-grade use cases for each mode. We additionally discuss the
primary challenges of extreme-scale AI-coupled HPC campaigns -- task
heterogeneity, adaptivity, performance -- and several framework and middleware
solutions which aim to address them. While both HPC workflow and AI/ML
computing paradigms are independently effective, we highlight how their
integration, and ultimate convergence, is leading to significant improvements
in scientific performance across a range of domains, ultimately resulting in
scientific explorations otherwise unattainable.
