论文标题
转移学习驱动的设计优化用于惯性限制融合
Transfer learning driven design optimization for inertial confinement fusion
论文作者
论文摘要
转移学习是创建预测模型的一种有前途的方法，将模拟和实验数据纳入共同框架。在这项技术中，首先在大型模拟数据库上训练神经网络，然后在稀疏的实验数据集上进行部分重新训练，以调整预测，以使其与现实更加一致。以前，该技术已用于创建欧米茄和NIF惯性限制融合（ICF）实验的预测模型，这些实验比单独模拟更准确。在这项工作中，我们进行了转移学习驱动的假设ICF运动，其中的目标是通过贝叶斯优化最大化实验性中子产量。在不到20个实验中，转移学习模型在适度的设计空间中达到最大可达到的产量的5％以内。此外，我们证明，这种方法比在ICF设计中常用的传统模型校准技术更有效地优化设计。这种ICF设计的方法可以在不确定性下对实验性能进行强有力的优化。
Transfer learning is a promising approach to creating predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural network is first trained on a large database of simulations, then partially retrained on sparse sets of experimental data to adjust predictions to be more consistent with reality. Previously, this technique has been used to create predictive models of Omega and NIF inertial confinement fusion (ICF) experiments that are more accurate than simulations alone. In this work, we conduct a transfer learning driven hypothetical ICF campaign in which the goal is to maximize experimental neutron yield via Bayesian optimization. The transfer learning model achieves yields within 5% of the maximum achievable yield in a modest-sized design space in fewer than 20 experiments. Furthermore, we demonstrate that this method is more efficient at optimizing designs than traditional model calibration techniques commonly employed in ICF design. Such an approach to ICF design could enable robust optimization of experimental performance under uncertainty.
