The data-driven future of high-energy-density physics
The data-driven future of high-energy-density physics
复制标题
DOI：
10.1038/s41586-021-03382-w
复制
发表时间：
2021-05-20
期刊：
影响因子：
64.8
通讯作者：
Williams, Ben
中科院分区：
文献类型：
--
作者：
Hatfield, Peter W.;Gaffney, Jim A.;Williams, Ben
高能密度物理学（英语：High-energy-density physics）是一门研究物质在极高温度和密度下的物理学。这样的条件会产生高度非线性的等离子体，其中几种通常可以相互独立处理的现象变得强烈耦合。对这些等离子体的研究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在理论上理解或在实验上优化。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，迄今为止，这些系统对于人类研究人员来说过于非线性。从基本的角度来看，我们的理解可以通过机器学习模型快速发现大型数据集中的复杂交互来提高。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），从而从基于人类的控制转向基于诊断数据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，最佳实践和支持合成诊断和数据分析方面为社区提出建议。
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.