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：我们提出了一种基于多保真度深度神经网络的新型持续学习方法。该方法通过学习先前训练模型输出与当前训练数据集上模型期望输出之间的相关性，有效限制了灾难性遗忘。独立应用时，该多保真度持续学习方法在多个数据集上展现出稳健的遗忘抑制效果。此外，我们证明该多保真度方法可与现有持续学习方法（包括回放与记忆感知突触）相结合，以进一步减轻灾难性遗忘。所提出的持续学习策略尤其适用于满足以下条件的物理问题：各域内数据遵循相同物理定律，或用于物理信息神经网络——因为在此类场景中，预期先前模型输出与当前训练域模型输出之间存在强相关性。