Joint deep reversible regression model and physics-informed unsupervised learning for temperature field reconstruction.

Temperature monitoring over heat source components in engineering systems, such as the energy system, electronic equipments, becomes essential to guarantee the working performance of these components. However, prior methods, which mainly use the interpolate estimation to reconstruct the overall temperature field from limited monitoring points, require large amounts of temperature tensors for an accurate estimation. This may affect the availability and reliability of the system. To solve the problem, this work develops a novel reconstruction method which joints the deep reversible regression model and physics-informed unsupervised learning for temperature field reconstruction of heat-source systems (TFR-HSS). Firstly, we define the TFR-HSS mathematically, numerically model the system with discrete grids, and hence transform the task as an image-to-image regression problem. Then, this work develops the deep reversible regression model which can better learn physical information, especially over the area near the boundaries of the system. Finally, this work proposes the physics-informed reconstruction loss with the physical characteristics of the system and learns the deep model without labelled samples. Experimental studies have conducted over typical two-dimensional heat-source systems to validate the effectiveness of the proposed method. Under the proposed method, the mean average error of the constructed temperature field can achieve about 0.1K, 50% lower than other methods. Besides, the proposed method takes 5.2 ms per sample for inference which can provide real-time predictions. • This work defines temperature field reconstruction of heat-source systems (TFR-HSS) task. • This work provides the mathematical formulation as well as numerically modelling of TFRHSS task. • This work further proposes the reversible regression model which can better fit for TFR-HSS task. • This work develops physics-informed reconstruction loss, which can train the deep model without labelled samples. [ABSTRACT FROM AUTHOR]