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使用机器学习加速格子结构的设计

Aldair E Gongora¹, Caleb Friedman², Deirdre K Newton²

¹Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA, 94550, USA. gongora1@llnl.gov.

Scientific reports

|June 13, 2024

概括

本研究引入了一种机器学习 (ML) 方法,与沙普利增量解释 (SHAP) 结合起来,以高效设计格子结构. 该方法通过解释变量并减少模拟需求,加速发现最佳设计.

科学领域:

工程工程师工程师工程师
材料科学材料科学材料科学
计算科学计算科学

背景情况:

格子结构提供了设计的多功能性,但快速,优化的机械性能设计是具有挑战性的.
越来越多的设计变量导致难以处理的设计空间,需要高效的计算方法.
现有的机器学习 (ML) 方法在模型解释和高效的培训数据策划方面面临挑战.

研究的目的:

为加速格子结构设计开发一个可解释的ML框架.
通过使用ML和可解释性技术来识别影响机械性能的关键设计变量.
为了提高训练数据策划的效率,用于网格设计中的优化任务.

主要方法:

结合基于ML的替代模型与Shapley添加式解释 (SHAP) 进行变量解释.
利用主动学习方法,特别是贝叶斯优化,以高效设计太空探索.
开发了一个集成ML的智能系统,用于设计变量发现和加速.

主要成果:

基于ML的替代模型显示出高预测准确度 (R2 > 0.95).
SHAP分析有效地确定了影响格子结构性能的设计变量.
与基于网格的搜索相比,主动学习减少了5倍的模拟需求.

结论:

集成ML和SHAP提供了一个强大的工具来解释网格结构中的设计变量.
积极学习策略显著提高了设计和优化过程的效率.
利用ML的智能设计系统对于加速定制格子结构的开发至关重要.

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