We introduce a novel continual learning method based on multifidelity deep neural networks. This method learns the correlation between the output of previously trained models and the desired output of the model on the current training dataset, limiting catastrophic forgetting. On its own the multifidelity continual learning method shows robust results that limit forgetting across several datasets. Additionally, we show that the multifidelity method can be combined with existing continual learning methods, including replay and memory aware synapses, to further limit catastrophic forgetting. The proposed continual learning method is especially suited for physical problems where the data satisfy the same physical laws on each domain, or for physics-informed neural networks, because in these cases we expect there to be a strong correlation between the output of the previous model and the model on the current training domain.

翻译：我们提出了一种新颖的基于多保真度深度神经网络的持续学习方法。该方法能够学习先前训练模型的输出与当前训练数据集上模型期望输出之间的相关性，从而限制灾难性遗忘。该多保真度持续学习方法本身在多个数据集上展现出稳健的遗忘抑制效果。此外，我们证明了多保真度方法可与现有持续学习方法（包括重放及记忆感知突触）相结合，以进一步限制灾难性遗忘。该方法特别适用于数据在各领域满足相同物理规律的物理问题，或物理信息神经网络，因为在这些情况下，先前模型输出与当前训练域模型输出之间预计存在强相关性。