Probabilistic machine learning techniques can learn both complex relations between input features and output quantities of interest as well as take into account stochasticity or uncertainty within a data set. In this initial work, we explore the use of one such probabilistic network, the mixture density network (MDN), to reproduce fission yields and their uncertainties. We study mass yields for the spontaneous fission of 252 Cf, exploring the number of training samples needed for converged predictions, how different levels of uncertainty propagate from the training set to the MDN predictions, and how well physical constraints of the yields—such as normalization and symmetry—are upheld by the algorithm. Finally, we test the ability of the MDN to interpolate between and extrapolate beyond samples in the training set using energy-dependent mass yields for the neutron-induced fission on 235 U. The MDN provides a reliable way to include and predict uncertainties a...

中文翻译：

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用混合密度网络量化裂变碎片质量产率的不确定性

概率机器学习技术可以学习输入特征和感兴趣的输出量之间的复杂关系，也可以考虑数据集中的随机性或不确定性。在这项初步工作中，我们探索了使用这样一种概率网络，即混合密度网络（MDN），来再现裂变产率及其不确定性。我们研究了252 Cf自发裂变的大量产量，探索了收敛预测所需的训练样本数量，从训练集到MDN预测如何传播不同程度的不确定性，以及产量的物理约束条件（例如归一化）如何算法保持对称性和对称性。最后，