  Thermal issue is of great importance during layout design of heat source
components in systems engineering, especially for high functional-density
products. Thermal analysis generally needs complex simulation, which leads to
an unaffordable computational burden to layout optimization as it iteratively
evaluates different schemes. Surrogate modeling is an effective way to
alleviate computation complexity. However, temperature field prediction (TFP)
with complex heat source layout (HSL) input is an ultra-high dimensional
nonlinear regression problem, which brings great difficulty to traditional
regression models. The Deep neural network (DNN) regression method is a
feasible way for its good approximation performance. However, it faces great
challenges in both data preparation for sample diversity and uniformity in the
layout space with physical constraints, and proper DNN model selection and
training for good generality, which necessitates efforts of both layout
designer and DNN experts. To advance this cross-domain research, this paper
proposes a DNN based HSL-TFP surrogate modeling task benchmark. With
consideration for engineering applicability, sample generation, dataset
evaluation, DNN model, and surrogate performance metrics, are thoroughly
studied. Experiments are conducted with ten representative state-of-the-art DNN
models. Detailed discussion on baseline results is provided and future
prospects are analyzed for DNN based HSL-TFP tasks.

Machine Learning;Artificial Intelligence;Neural and Evolutionary Computing