This study proposes an enhanced co-training artificial neural network (Enhanced Co-ANN) guided by the physical attention mechanisms for the effective thermal conductivity prediction of high filler volume fraction polymer/BN composites. The thermal conductivity of BN composites is influenced by multiple factors, including the morphology of the fillers, interface thermal resistance, and experimental noise. This model tackles complex physical processes by integrating a customized multi-head physical attention layer to emphasize key features, along with a physics-constrained loss function to ensure prediction consistency. A collaborative training strategy based on curriculum learning and consistency discrimination is adopted. The model is optimized using 3174 labeled experimental datasets and 50,000 unlabeled data generated from physical models. Weight distribution is systematically designed across three core levels: model architecture, loss function, and training strategy. This approach differs from traditional parameter weight adjustments, as it emphasizes key features, especially volume fraction (vf), and balances different learning objectives through a physically guided mechanism and dynamic training strategies. Attention visualization indicates that the model adaptively focuses on the volume fraction of the packing material and the interface effect, verifying the effectiveness of the physically guided design. Six groups of samples with different packing volume fractions were made for testing and validation. This model has high accuracy (R² = 0.982; MAE = 0.045 W/m K) and is extremely consistent with physical laws. This network framework provides a method with broad application prospects for the rapid calculation, screening, and efficient design of high-performance polymer/BN thermal conductive materials.
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