Learning topology optimization process via convolutional long-short-term memory autoencoder-decoder

This article proposed an autoencoder-decoder architecture with convolutional long-short-term memory (ConvLSTM) cell for the purpose of learning topology optimization iterations. The overall topology optimization process is treated as time-series data, with each iteration as a single step. The first few steps are fed into the encoder to generate encoder embedding, which is fed into the decoder. The decoder uses the encoder embedding as input and generates the result at each future step until the end of iteration. To train the proposed neural network, a large dataset is generated by a conventional topology optimization method, that is, solid isotropic material with penalization for intermediate densities, with randomly picked boundary conditions, load conditions, and volume constraints. Unlike other deep learning models introduced before, the proposed method can learn each topology optimization step iteratively and present the full optimization path. Furthermore, the proposed method can be extended to give solutions to unseen boundary and load conditions with a significant reduction in computation cost in a little sacrifice on the performance of the optimum design.