Theory of Machine Learning Assisted Structural Optimization Algorithm and Its Application

Abstract

The machine learning assisted structural optimization (MLASO) algorithm has recently been proposed to expedite topology optimization. In the MLASO algorithm, the machine learning model learns and predicts the update of the chosen optimization quantity in routine and prediction iterations. The routine and prediction iterations are activated with a predefined learning and predicting scheme; and in the prediction iterations, the design variable can be updated using the predicted quantity without running a finite element analysis and sensitivity analysis, and thus the computational time can be saved. Based on the MLASO algorithm, this work first proposes a novel generic criterion-driven learning and predicting (CDLP) scheme that allows the algorithm to autonomously activate prediction iterations in the solution procedure. Second, this work presents the convergence analysis and the computational efficiency analysis of the MLASO algorithm with the CDLP scheme. The MLASO algorithm is then embedded within the solid isotropic material with penalization topology optimization method to solve two-dimensional and three-dimensional problems. Numerical examples and results demonstrate the prediction accuracy and the computational efficiency of the MLASO algorithm, and that the CDLP scheme can remarkably improve the computational efficiency of the MLASO algorithm.