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Apr, 2023
物理系统连续学习的多保真度方法
A multifidelity approach to continual learning for physical systems
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Amanda Howard, Yucheng Fu, Panos Stinis
TL;DR
提出一种基于多保真深度神经网络的连续学习方法，限制灾难性遗忘，并能与已有的连续学习方法（包括重放和记忆感知突触）结合使用。该方法特别适合解决物理问题和基于物理的神经网络。
Abstract
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 o
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