A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
An online autonomous learning and prediction scheme for machine learning assisted structural optimization
https://orcid.org/0000-0003-4307-4221
Abstract In this paper, an online autonomous learning and prediction (ALP) scheme is proposed for machine learning assisted structural optimization (MLaSO) to reduce total computational time with controlled accuracy of objective function prediction. In MLaSO, information in selected routine iterations is used via online learning to train a Neural Network, which is then used to perform online prediction of information in subsequent other iterations. Although using MLaSO with pre-defined learning and prediction scheme can save total computational time, it is difficult to determine the most suitable learning and prediction scheme prior to solving an optimization problem. The present ALP method enables autonomous activation of online learnings and predictions via maximizing specific computational efficiency while constraining the accuracy of the objective function prediction. Four numerical 2D and 3D examples are presented to demonstrate the computational benefits of integrating ALP with gradient based MLaSO method. The present numerical results demonstrate that: (a) using ALP in MLaSO can save more computational time than using the pre-defined scheme with similar prediction accuracy, and (b) more computational time can be saved by allowing larger tolerance in the accuracy of objective function prediction.
Highlights A framework to accelerate gradient-based structural optimization assisted by machine learning. An online autonomous learning and prediction scheme for maximum efficiency with accuracy control. Pareto solutions for computational timesaving and accuracy for selected 2D and 3D examples.
An online autonomous learning and prediction scheme for machine learning assisted structural optimization
https://orcid.org/0000-0003-4307-4221
Abstract In this paper, an online autonomous learning and prediction (ALP) scheme is proposed for machine learning assisted structural optimization (MLaSO) to reduce total computational time with controlled accuracy of objective function prediction. In MLaSO, information in selected routine iterations is used via online learning to train a Neural Network, which is then used to perform online prediction of information in subsequent other iterations. Although using MLaSO with pre-defined learning and prediction scheme can save total computational time, it is difficult to determine the most suitable learning and prediction scheme prior to solving an optimization problem. The present ALP method enables autonomous activation of online learnings and predictions via maximizing specific computational efficiency while constraining the accuracy of the objective function prediction. Four numerical 2D and 3D examples are presented to demonstrate the computational benefits of integrating ALP with gradient based MLaSO method. The present numerical results demonstrate that: (a) using ALP in MLaSO can save more computational time than using the pre-defined scheme with similar prediction accuracy, and (b) more computational time can be saved by allowing larger tolerance in the accuracy of objective function prediction.
Highlights A framework to accelerate gradient-based structural optimization assisted by machine learning. An online autonomous learning and prediction scheme for maximum efficiency with accuracy control. Pareto solutions for computational timesaving and accuracy for selected 2D and 3D examples.
An online autonomous learning and prediction scheme for machine learning assisted structural optimization
https://orcid.org/0000-0003-4307-4221
Abstract In this paper, an online autonomous learning and prediction (ALP) scheme is proposed for machine learning assisted structural optimization (MLaSO) to reduce total computational time with controlled accuracy of objective function prediction. In MLaSO, information in selected routine iterations is used via online learning to train a Neural Network, which is then used to perform online prediction of information in subsequent other iterations. Although using MLaSO with pre-defined learning and prediction scheme can save total computational time, it is difficult to determine the most suitable learning and prediction scheme prior to solving an optimization problem. The present ALP method enables autonomous activation of online learnings and predictions via maximizing specific computational efficiency while constraining the accuracy of the objective function prediction. Four numerical 2D and 3D examples are presented to demonstrate the computational benefits of integrating ALP with gradient based MLaSO method. The present numerical results demonstrate that: (a) using ALP in MLaSO can save more computational time than using the pre-defined scheme with similar prediction accuracy, and (b) more computational time can be saved by allowing larger tolerance in the accuracy of objective function prediction.
Highlights A framework to accelerate gradient-based structural optimization assisted by machine learning. An online autonomous learning and prediction scheme for maximum efficiency with accuracy control. Pareto solutions for computational timesaving and accuracy for selected 2D and 3D examples.
An online autonomous learning and prediction scheme for machine learning assisted structural optimization
Xing, Yi (author) / Tong, Liyong (author)
Thin-Walled Structures ; 184
2022-12-26
Article (Journal)
Electronic Resource
English
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