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In this investigation, an integrated physics-informed deep learning and topology optimization approach for solving density-based topology designs is presented to accomplish efficiency and flexibility. In every iteration, the neural network generates feasible topology designs, and then the topology performance is evaluated using the finite element method. Unlike the data-driven methods where the loss functions are…