  The study of plasma physics under conditions of extreme temperatures,
densities and electromagnetic field strengths is significant for our
understanding of astrophysics, nuclear fusion and fundamental physics. These
extreme physical systems are strongly non-linear and very difficult to
understand theoretically or optimize experimentally. Here, we argue that
machine learning models and data-driven methods are in the process of reshaping
our exploration of these extreme systems that have hitherto proven far too
non-linear for human researchers. From a fundamental perspective, our
understanding can be helped by the way in which machine learning models can
rapidly discover complex interactions in large data sets. From a practical
point of view, the newest generation of extreme physics facilities can perform
experiments multiple times a second (as opposed to ~daily), moving away from
human-based control towards automatic control based on real-time interpretation
of diagnostic data and updates of the physics model. To make the most of these
emerging opportunities, we advance proposals for the community in terms of
research design, training, best practices, and support for synthetic
diagnostics and data analysis.
