Method for directly and instantaneously predicting conductive heat transfer topologies by using supervised deep learning

A supervised deep learning predictor was developed to directly, intelligently, and instantaneously infer conductive heat transfer topologies. The architecture of the proposed supervised predictor consists of an encoder to decrease the dimensionality of the input data and a decoder. The high accuracy of the predictor is enabled by three parallel linked supervised deep learning predictors. Once the predictor has been trained and the physical parameters of the heat conduction problem, such as the boundary and constraint conditions, have been provided as input, the predictor directly and instantly outputs the optimized topology.