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Surrogate Model via Artificial Intelligence Method for Accelerating Screening Materials and Performance Prediction

机译：通过人工智能方法加速筛选材料和性能预测的代理模型

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摘要

Predicting the performance of mechanical properties is an important and current issue in the field of engineering and materials science, but traditional experiments and modeling calculations often consume large amounts of time and resources. Therefore, it is imperative to use appropriate methods to accelerate the process of material selection and design. The artificial intelligence method, particularly deep learning models, has been verified as an effective and efficient method for handling computer vision and neural language problems. In this paper, a deep learning surrogate model (DLS) is proposed for predicting the mechanical performance of materials, that is, the maximum stress value under complex working conditions. The DLS can reproduce the finite element analysis model results with 98.79% accuracy. The results show that deep learning has great potential. This research also provides a new approach for material screening in practical engineering.
机译：预测机械性能的性能是工程和材料科学领域的重要和当前问题，但传统的实验和建模计算通常会消耗大量的时间和资源。 因此，使用适当的方法来加速材料选择和设计的过程。 人工智能方法，特别是深度学习模型，已被验证为处理计算机视觉和神经语言问题的有效和有效的方法。 在本文中，提出了一种用于预测材料的机械性能的深度学习替代模型（DLS），即复杂工作条件下的最大应力值。 DLS可以再现有限元分析模型，精度为98.79％。 结果表明，深度学习具有很大的潜力。 该研究还提供了一种在实际工程中筛选的新方法。

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  • 来源
    《Advanced Functional Materials》 |2021年第8期|2006245.1-2006245.10|共10页
  • 作者

    Wang Tian; Shao Mingqi; Guo Rong; Tao Fei; Zhang Gang; Snoussi Hichem; Tang Xingling;

  • 作者单位

    Beihang Univ Inst Artificial Intelligence Beijing 100191 Peoples R China|Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China;

    Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China;

    Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China;

    Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China;

    Inst High Performance Comp A STAR Singapore 138632 Singapore;

    Univ Technol Troyes Inst Charles Delaunay LM2S FRE CNRS 2019 F-10004 Troyes France;

    China Nucl Power Engn Co Ltd Beijing 100840 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    artificial intelligence; deep learning; finite element analysis; surrogate model;

    机译：人工智能;深入学习;有限元分析;代理模型;

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