High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics—however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to 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 proved far too nonlinear for human researchers. From a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus 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 suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis.

高能密度物理学是研究在极高温度和密度下的物质的物理学领域。这样的条件会产生高度非线性的等离子体，其中通常可以相互独立处理的几种现象变得强耦合。研究这些等离子体对于我们理解天体物理学、核聚变和基础物理学非常重要——然而，这些极端物理系统中存在的非线性和强耦合使得它们很难从理论上理解或通过实验进行优化。在这里，我们认为机器学习模型和数据驱动方法正在重塑我们对这些极端系统的探索，这些系统迄今已被证明对人类研究人员来说过于非线性。从基本面来看，机器学习模型可以快速发现大型数据集中的复杂交互的方式可以提高我们的理解。从实用的角度来看，最新一代的极端物理设施可以每秒进行多次实验（而不是大约每天一次），从而从以人为本的控制转向基于诊断数据实时解释的自动控制和物理模型的更新。为了充分利用这些新出现的机会，我们在研究设计、培训、最佳实践以及对综合诊断和数据分析的支持方面向社区提出建议。最新一代的极端物理设施可以每秒进行多次实验（而不是大约每天一次），从而从基于人的控制转向基于诊断数据的实时解释和物理模型更新的自动控制。为了充分利用这些新出现的机会，我们在研究设计、培训、最佳实践以及对综合诊断和数据分析的支持方面向社区提出建议。最新一代的极端物理设施可以每秒进行多次实验（而不是大约每天一次），从而从基于人的控制转向基于诊断数据的实时解释和物理模型更新的自动控制。为了充分利用这些新出现的机会，我们在研究设计、培训、最佳实践以及对综合诊断和数据分析的支持方面向社区提出建议。