  Temperature field reconstruction of heat source systems (TFR-HSS) with
limited monitoring sensors occurred in thermal management plays an important
role in real time health detection system of electronic equipment in
engineering. However, prior methods with common interpolations usually cannot
provide accurate reconstruction performance as required. In addition, there
exists no public dataset for widely research of reconstruction methods to
further boost the reconstruction performance and engineering applications. To
overcome this problem, this work develops a machine learning modelling
benchmark for TFR-HSS task. First, the TFR-HSS task is mathematically modelled
from real-world engineering problem and four types of numerically modellings
have been constructed to transform the problem into discrete mapping forms.
Then, this work proposes a set of machine learning modelling methods, including
the general machine learning methods and the deep learning methods, to advance
the state-of-the-art methods over temperature field reconstruction. More
importantly, this work develops a novel benchmark dataset, namely Temperature
Field Reconstruction Dataset (TFRD), to evaluate these machine learning
modelling methods for the TFR-HSS task. Finally, a performance analysis of
typical methods is given on TFRD, which can be served as the baseline results
on this benchmark.
