Thermal Simulation Computing Network Based on Deep Learning

The heat and moisture transfer simulation about human body and clothing is a method to simulate the regulation mechanism of human body, and heat and moisture performance of clothing in thermal environment by using computer technology. According to the simulation results, it can be used to the monitor and predicate the key physiological data of human body, and the functional clothing design. Some mathematical models about this area have been built. However, a large number of differential equations are involved to be used to the numerical computation in the simulation process, which leads to high computational complexity, large memo-ry consumption and long computing time. With the expansion of computing scale, these problems will become more obvious and directly affect the practical application of simulation models. The main work of this paper is to study the efficient solution method based on neural network, and proposed a deep learning-based differential equation computing network. The network uses differential equation as the constraint of the loss function of neural network, and integrates the differential equation into the training and prediction process of the network. The experimental results show that the proposed network can reduce the computational complexity of the simulation model and improve the computational efficiency.