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A machine learning-based surrogate model for optimization of truss structures with geometrically nonlinear behavior

机译：基于机器学习的代理模型，用于优化具有几何非线性行为的桁架结构

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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.
机译：通过使用增量迭代解决方案技术，几何非线性结构的设计优化是众所周知的计算昂贵的问题。为了有效地处理问题，优化算法需要确保找到计算时间和解决方案质量之间的权衡。在本研究中，开发了一种与差分演进（DE）算法集成的深神经网络（DNN）的代理模型，用于解决位移约束下几何非线性空间桁架的最佳设计问题，并指代DNN-的方法德。因此，该代理模型也被称为深神经网络，建立以取代传统的有限元分析（FEAS）。每个数据集都是根据FEA创建的，该FEA采用Lagrangian配方和Arc-Lenge过程。给出了几个数值例子来证明所提出的范例的效率和有效性。这些结果表明，所提出的方法不仅会急剧降低计算成本，而且还可以保证融合。

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