Resumen
Engineering thermal management (ETM) is one of the critical tasks for quality control and system surveillance in many industries, and acquiring the temperature field and its evolution is a prerequisite for efficient thermal management. By harnessing the sensing data from sensor networks, an unprecedented opportunity has emerged for an accurate estimation of the temperature field. However, limited resources of sensor deployment and computation capacity pose a great challenge while modeling the spatiotemporal dynamics of the temperature field. This paper presents a novel temperature field estimation approach to describe the dynamics of a temperature field by combining a physics-specific model and a spatiotemporal Gaussian process. To reduce the computational burden while dealing with a large set of spatiotemporal data, we employ a tapering covariance function and develop an associated parameter estimation procedure. We introduce a case study of grain storage to show the effectiveness and efficiency of the proposed approach.