Staged Depth Training: A Representation Curriculum for PINNs

The paper targets physics-informed neural networks and focuses on the quality of learned representations during PDE solution fitting. It proposes a representation curriculum in which representations are explicitly learned, transferred independently of their predictors, and progressively refined. The method is realized as Staged Depth Training (SDT), which begins by training a shallow prefix before proceeding through deeper stages.