论文标题
机器学习如何帮助复合材料和结构的设计和分析？
How machine learning can help the design and analysis of composite materials and structures?
论文作者
论文摘要
由于广泛的数字数据，增长的计算能力和高级算法，机器学习模型越来越多地用于许多工程领域。人工神经网络（ANN）是近年来最受欢迎的机器学习模型。尽管许多ANN模型已用于复合材料和结构的设计和分析，但仍然存在一些未解决的问题，阻碍了ANN模型在复合材料和结构的实用设计和分析中的接受。此外，新兴的机器学习技术正在基于数据的设计范式中发布新的机会和挑战。本文旨在在非线性本构建模，多尺度替代建模以及复合材料和结构的设计优化中对ANN模型进行最新文献综述。这篇综述旨在将重点放在对ANN模型对上述问题的一般框架和好处的讨论上。此外，确定和讨论了每个关键问题中的挑战和机遇。预计本文将对未来的研究范围和新方向进行讨论，以实现对复合材料和结构的有效，健壮和准确的数据驱动设计和分析。
Machine learning models are increasingly used in many engineering fields thanks to the widespread digital data, growing computing power, and advanced algorithms. Artificial neural networks (ANN) is the most popular machine learning model in recent years. Although many ANN models have been used in the design and analysis of composite materials and structures, there are still some unsolved issues that hinder the acceptance of ANN models in the practical design and analysis of composite materials and structures. Moreover, the emerging machine learning techniques are posting new opportunities and challenges in the data-based design paradigm. This paper aims to give a state-of-the-art literature review of ANN models in the nonlinear constitutive modeling, multiscale surrogate modeling, and design optimization of composite materials and structures. This review has been designed to focus on the discussion of the general frameworks and benefits of ANN models to the above problems. Moreover, challenges and opportunities in each key problem are identified and discussed. This paper is expected to open the discussion of future research scope and new directions to enable efficient, robust, and accurate data-driven design and analysis of composite materials and structures.
