  The convergence of HPC and data-intensive methodologies provide a promising
approach to major performance improvements. This paper provides a general
description of the interaction between traditional HPC and ML approaches and
motivates the Learning Everywhere paradigm for HPC. We introduce the concept of
effective performance that one can achieve by combining learning methodologies
with simulation-based approaches, and distinguish between traditional
performance as measured by benchmark scores. To support the promise of
integrating HPC and learning methods, this paper examines specific examples and
opportunities across a series of domains. It concludes with a series of open
computer science and cyberinfrastructure questions and challenges that the
Learning Everywhere paradigm presents.
