P-TTAN: A novel neural network optimized for thermal feature perception and representation in 3D temperature predictions

Thermal properties of armored vehicles are significant for both offensive and defensive strategies in modern warfare and surveillance. Existing methods struggle with the computational complexity, weak generalization capabilities and lack the real-time capability necessary for dynamic battlefield conditions. This paper presents a novel Parametric Transient Thermophysical Attention Network (P-TTAN), which can predict three-dimensional (3D) transient temperature fields of armored vehicles. Utilizing 3D shape data, material parameters, and environmental meteorological conditions, P-TTAN demonstrates significant advancements in predictive accuracy and computational efficiency. Inspired by PointNet structure, our network integrates shape and thermal features effectively, enhancing its ability to process complex 3D point cloud data. By designing a newly thermal feature extraction module, P-TTAN can recognize both transient and invariant parameters via an improved Transformer. Moreover, P-TTAN incorporates heat conduction differential equations via a simplified method, enhancing the interpretability and reliability of its predictions while reducing computational costs and improving its applicability. The results on the test dataset indicate that the 3D temperature field predictions made by P-TTAN are markedly superior to other state-of-the-art networks, achieving a Mean Absolute Error of just 0.5687 K. This capability marks it as a highly effective tool for real-time thermal analysis in both military and civilian contexts.