Physics-Informed Deep Reversible Regression Model for Temperature Field Reconstruction of Heat-Source Systems

Temperature monitoring during the life time of heat-source components in engineering systems becomes essential to ensure the normal work and even the long working life of the heat sources. However, prior methods, which mainly use the interpolate estimation, require large amounts of temperature tensors for an accurate estimation. To solve this problem, this work develops a novel physics-informed deep surrogate models for temperature field reconstruction. First, we defines the temperature field reconstruction task of heat-source systems. Then, this work develops the deep surrogate model mapping for the proposed task. Finally, considering the physical properties of heat transfer, this work proposes four different losses and joint learns the deep surrogate model with these losses. Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness and efficiency of the proposed physics-informed deep surrogate models for temperature field reconstruction.