The data-driven future of high energy density physics

TL;DR

Machine learning and data-driven methods are transforming high energy density physics by enabling rapid analysis, understanding complex non-linear systems, and automating experimental control in extreme plasma conditions.

Contribution

The paper advocates for integrating machine learning into high energy density physics research, proposing new research practices, training, and support for data analysis and diagnostics.

Findings

Machine learning models can discover complex interactions in large plasma data sets.

Real-time diagnostic data enables automatic control of experiments.

Data-driven approaches accelerate understanding of non-linear plasma phenomena.

Abstract

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.