Building a sustainable burn platform in inertial confinement fusion (ICF) requires an understanding of the complex coupling of physical processes and the effects that key experimental design changes have on implosion performance. While simulation codes are used to model ICF implosions, incomplete physics and the need for approximations deteriorate their predictive capability. Identification of relationships between controllable design inputs and measurable outcomes can help guide the future design of experiments and development of simulation codes, which can potentially improve the accuracy of the computational models used to simulate ICF implosions. In this article, we leverage developments in machine learning (ML) and methods for ML feature importance/sensitivity analysis to identify complex relationships in ways that are difficult to process using expert judgment alone. We present work using random forest (RF) regression for prediction of yield, velocity, and other experimental outcomes given a suite of design parameters, along with an assessment of important relationships and uncertainties in the prediction model. We show that RF models are capable of learning and predicting on ICF experimental data with high accuracy, and we extract feature importance metrics that provide insight into the physical significance of different controllable design inputs for various ICF design configurations. These results can be used to augment expert intuition and simulation results for optimal design of future ICF experiments.

中文翻译：

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使用机器学习探索 ICF 输出对实验中设计参数的敏感性


在惯性约束聚变（ICF）中构建可持续燃烧平台需要了解物理过程的复杂耦合以及关键实验设计变化对内爆性能的影响。虽然仿真代码用于模拟 ICF 内爆，但不完整的物理学和对近似的需要会降低其预测能力。识别可控设计输入和可测量结果之间的关系有助于指导未来的实验设计和模拟代码的开发，从而有可能提高用于模拟 ICF 内爆的计算模型的准确性。在本文中，我们利用机器学习 (ML) 的发展和 ML 特征重要性/敏感性分析方法来识别复杂关系，而仅使用专家判断很难处理这些关系。我们提出使用随机森林（RF）回归来预测给定一组设计参数的产量、速度和其他实验结果的工作，以及对预测模型中的重要关系和不确定性的评估。我们证明 RF 模型能够高精度地学习和预测 ICF 实验数据，并且我们提取特征重要性度量，以深入了解各种 ICF 设计配置的不同可控设计输入的物理意义。这些结果可用于增强专家直觉和模拟结果，以实现未来 ICF 实验的优化设计。