论文标题
高能密度hohlraum设计使用前向和逆深神经网络
High-Energy Density Hohlraum Design Using Forward and Inverse Deep Neural Networks
论文作者
论文摘要
我们提出了一项研究，以使用机器学习来增强不透明度测量实验的Hohlraum设计。对于不透明度实验，我们希望有一个Hohlraum，当它的内壁被当时的点火设施（NIF）激光照亮时，它将产生高辐射通量，将中心样品加热到在测量时间窗口上恒定的温度。鉴于基线HOHLRAUM设计和计算模型，我们训练一个深神网络，以预测Dante诊断测量的辐射温度的时间演变。这使我们能够快速探索设计空间并确定调整设计参数的效果。我们还构建了一个“反向”机器学习模型，该模型可以预测辐射温度所需时间历史的设计参数。使用机器学习模型进行的计算表明，基线hohlraum的性能提高将减少实验不透明度测量中的不确定性。
We present a study of using machine learning to enhance hohlraum design for opacity measurement experiments. For opacity experiments we desire a hohlraum that, when its interior walls are illuminated by theNational Ignition Facility (NIF) lasers, will produce a high radiation flux that heats a central sample to a temperature that is constant over a measurement time window. Given a baseline hohlraum design and a computational model, we train a deep neural network to predict the time evolution of the radiation temperature as measured by the Dante diagnostic. This enables us to rapidly explore design space and determine the effect of adjusting design parameters. We also construct an "inverse" machine learning model that predicts the design parameters given a desired time history of radiation temperature. Calculations using the machine learning model demonstrate that improved performance over the baseline hohlraum would reduce uncertainties in experimental opacity measurements.
