Investigation into the topology optimization for conductive heat transfer based on deep learning approach

A deep learning approach combining with the traditional solid isotropic material with penalization (SIMP) method is presented in this paper to accelerate the topology optimization of the conductive heat transfer. This deep learning predictor is structured based on the deep fully convolutional neural network. The validity and accuracy of this deep learning approach is investigated based on the typical 'Volume-Point' heat conduction problems. The time consumption of the optimization process will be reduced significantly by introducing the deep learning approach.