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This paper proposes a self-supervised physics-informed neural network (PINN) framework with a learnable blending neuron to adaptively balance physics-based and data-driven losses, and integrates transfer learning to improve efficiency under data scarcity. It is validated on liquid-metal miniature heat sink CFD data with only 87 datapoints, achieving under 8% error.