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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Theory of Machine Learning Assisted Structural Optimization Algorithm and Its Application


    Contributors:
    Xing, Yi (author) / Tong, Liyong (author)

    Published in:

    AIAA Journal ; 61 , 10 ; 4664-4680


    Publication date :

    2023-07-14


    Size :

    17 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Dynamic Machine Learning Global Optimization Algorithm and Its Application to Aerodynamics

    Zhang, Zi-Qing / Li, Pei-Jing / Li, Qing-Kuo et al. | AIAA | 2023



    Machine-Learning-Assisted Optimization of Aircraft Trajectories Under Realistic Constraints

    Xu, Yifan / Wandelt, Sebastian / Sun, Xiaoqian et al. | AIAA | 2023


    Application of Machine Learning Algorithm in Optimization of PSV for 110000Dwt Oil Tanker

    Xiyang, Liu / Jingpu, Chen / Wenyu, Sun et al. | British Library Conference Proceedings | 2020