桁架
替代模型
人工神经网络
有限元法
趋同(经济学)
非线性系统
数学优化
计算机科学
流离失所(心理学)
算法
数学
人工智能
工程类
结构工程
心理学
物理
量子力学
经济
心理治疗师
经济增长
作者
Hau T.,Joo‐Won Kang,Jaehong Lee
标识
DOI:10.1016/j.finel.2021.103572
摘要
Design optimization of geometrically nonlinear structures is well known as a computationally expensive problem by using incremental-iterative solution techniques. To handle the problem effectively the optimization algorithm needs to ensure that the trade-off between the computational time and the quality of the solution is found. In this study, a deep neural network (DNN)-based surrogate model which integrates with differential evolution (DE) algorithm is developed and applied for solving the optimum design problem of geometrically nonlinear space truss under displacement constraints and refer to the approach as DNN-DE. Accordingly, this surrogate model, also is known as a deep neural network, is established to replace conventional finite element analyses (FEAs). Each dataset is created based on FEA which employs the total Lagrangian formulation and the arc-length procedure. Several numerical examples are given to demonstrate the efficiency and validity of the proposed paradigm. These results indicate that the proposed approach not only reduces the computational cost dramatically but also guarantees convergence.
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