Fig. 1From: Transfer learning for deep neural network-based partial differential equations solvingOverview of the transfer learning approach. Instead of initializing the entire network randomly, the usual transfer approach is to train a base network on a large dataset and then copy (transfer) its first n layers to the first n layers of a target network. The remaining layers are randomly initialized and trained toward the target taskBack to article page