Deep-Learning-Enabled Simulated Annealing for Topology Optimization
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Topology optimization by distributing materials in a domain requires stochastic optimizers to solve highly complicated problems. However, solving such problems requires millions of finite element calculations with hundreds of design variables or more involved , whose computational cost is huge and often unacceptable. To speed up computation, here we report a method to integrate deep learning into stochastic optimization algorithm. A Deep Neural Network (DNN) learns and substitutes the objective function by forming a loop with Generative Simulated Annealing (GSA). In each iteration, GSA uses DNN to evaluate the objective function to obtain an optimized solution, based on which new training data are generated; thus, DNN enhances its accuracy and GSA could accordingly improve its solution in next iteration until convergence. Our algorithm was tested by compliance minimization problems and reduced computational time by over two orders of magnitude. This approach sheds light on solving large multi-dimensional optimization problems.
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