神经网络在许多科学领域都变得流行，因为它们是有前途，可靠和强大的工具。在这项工作中，我们研究了数据增强对核物理数据神经网络模型的预测能力的影响。我们提供两种不同的数据增强技术，并根据不同的深度，优化器，激活功能和随机种子值进行详细的分析，以显示模型的成功和鲁棒性。首次使用实验不确定性进行数据扩展，人为地增强了训练数据集的大小，并且研究了测试集的模型预测与实验数据之间的根平方误差的变化。我们的结果表明，数据增强降低了预测错误，稳定模型并防止过度拟合。还测试了AME2020质量表中新测得的核的MLP模型的外推能力，并显示通过使用数据增强来显着改善预测。

最近，神经网络发生了重大发展。因此，神经网络经常在物理文献中使用。这项工作估计了使用神经网络从介子和巴里昂群众产生的异国情调的哈德子的质量。随后，使用最近提出的人工数据增强技术增加了数据数量。我们已经观察到，使用增强数据，神经网络的预测能力提高了。这项研究表明，数据增强技术在改善神经网络预测中起着至关重要的作用。此外，神经网络可以对异国情调的哈德子做出合理的预测，双重迷人和双重底层的重子。结果也与高斯过程和组成夸克模型相媲美。

The ability to estimate epistemic uncertainty is often crucial when deploying machine learning in the real world, but modern methods often produce overconfident, uncalibrated uncertainty predictions. A common approach to quantify epistemic uncertainty, usable across a wide class of prediction models, is to train a model ensemble. In a naive implementation, the ensemble approach has high computational cost and high memory demand. This challenges in particular modern deep learning, where even a single deep network is already demanding in terms of compute and memory, and has given rise to a number of attempts to emulate the model ensemble without actually instantiating separate ensemble members. We introduce FiLM-Ensemble, a deep, implicit ensemble method based on the concept of Feature-wise Linear Modulation (FiLM). That technique was originally developed for multi-task learning, with the aim of decoupling different tasks. We show that the idea can be extended to uncertainty quantification: by modulating the network activations of a single deep network with FiLM, one obtains a model ensemble with high diversity, and consequently well-calibrated estimates of epistemic uncertainty, with low computational overhead in comparison. Empirically, FiLM-Ensemble outperforms other implicit ensemble methods, and it and comes very close to the upper bound of an explicit ensemble of networks (sometimes even beating it), at a fraction of the memory cost.