A physics-regularized optimal transport map transfers neural operators across domains with limited target data, outperforming fine-tuning and feature-alignment baselines.
Representation subspace distance for domain adaptation regression
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A Physics-preserved Transfer Learning Method for Differential Equations
A physics-regularized optimal transport map transfers neural operators across domains with limited target data, outperforming fine-tuning and feature-alignment baselines.