基于机器学习的围动力代用模型,用于表征材料和结构的变形与失效

IF 8.7 2区 工程技术 Q1 Mathematics Engineering with Computers Pub Date : 2024-06-19 DOI:10.1007/s00366-024-02014-x
Han Wang, Liwei Wu, Dan Huang, Jianwei Chen, Junbin Guo, Chuanqiang Yu, Yayun Li, Yichang Wu
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引用次数: 0

摘要

在数据驱动神经网络中构建代用模型时,有必要确定输入特征和输出结果。由于在使用代用力学模型时,特征规律会受到限制,因此在传统连续介质力学框架内建立一套自然特征来准确描述材料和结构的失效过程仍然是一个挑战。为了应对这一挑战,本研究提出了一种在周动态-深度学习框架内构建代用模型的稳健方法,该方法能够明确地表示材料的变形和失效。所提出的代用模型整合了参考数据和当前配置数据,以完善输入特征,从而加强模型训练。我们在激活函数之前加入了批量归一化层,以缓解收敛速度慢、预测精度低以及因损伤数据集数值差异大而导致的过拟合等常见问题。此外,还对几个典型实例进行了数值分析,以验证本模型和方法的有效性和通用性。结果表明,该模型在训练集和测试集中都具有很高的准确性,证实了其出色的泛化能力和在材料失效分析中的巨大潜力。根据这项工作,通过考虑强化学习和符号空间,基于机器学习的周动态代用模型可以进一步推导出更多的周动态表达式,从而有可能将其应用范围扩大到更广泛的机械问题上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A machine-learning-based peridynamic surrogate model for characterizing deformation and failure of materials and structures

It is necessary to determine the input features and output results when constructing a surrogate model within the data-driven neural network. Since the law of features would be restrained when the surrogate mechanical model is employed, it is still a challenge to build a set of natural features to accurately describe the failure process of materials and structures within the traditional continuum mechanics framework. To address this challenge, a robust approach for constructing a surrogate model within the peridynamic-deep learning framework is proposed in this study, which is capable of representing material deformation and failure explicitly. The presented surrogate model integrates both reference and current configuration data to refine input features, enhancing model training. We incorporate a batch-normalization layer before the activation function to mitigate common issues such as slow convergence, low prediction accuracy, and overfitting due to the large numerical differences in the damage dataset. Additionally, numerical analyses on several typical examples are performed to validate the effectiveness and generality of the present model and methodology. The results demonstrate high accuracy in the training set as well as the testing set, confirming the model’s excellent generalization ability and significant potential for material failure analysis. According to this work, more peridynamic expressions can be further derived in the machine-learning-based peridynamic surrogate model by considering the reinforcement learning and symbol space, to potentially broaden its applicability to a wider range of mechanical issues.

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来源期刊
Engineering with Computers
Engineering with Computers 工程技术-工程:机械
CiteScore
16.50
自引率
2.30%
发文量
203
审稿时长
9 months
期刊介绍: Engineering with Computers is an international journal dedicated to simulation-based engineering. It features original papers and comprehensive reviews on technologies supporting simulation-based engineering, along with demonstrations of operational simulation-based engineering systems. The journal covers various technical areas such as adaptive simulation techniques, engineering databases, CAD geometry integration, mesh generation, parallel simulation methods, simulation frameworks, user interface technologies, and visualization techniques. It also encompasses a wide range of application areas where engineering technologies are applied, spanning from automotive industry applications to medical device design.
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