A physics-regularized optimal transport map transfers neural operators across domains with limited target data, outperforming fine-tuning and feature-alignment baselines.
Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems
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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.