A deep neural network surrogate modeling benchmark for temperature field prediction of heat source layout

The thermal issue is of great importance during the layout design of heat source components in systems engineering, especially for high functional-density products. Thermal analysis requires complex simulation, which leads to an unaffordable computational burden to layout optimization as it iteratively evaluates different schemes. Surrogate modeling is an effective method for alleviating computation complexity. However, the 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 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 the efforts of layout designers 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 investigated. Experiments are conducted with ten representative state-of-the-art DNN models. A detailed discussion on baseline results is provided, and future prospects are analyzed for DNN-based HSL-TFP tasks.