在材料和制造研究中生成和利用AI/ML模型的解释
Erick J Braham1,2, Jennifer M Ruddock1,2, James O Hardin1
1Materials and Manufacturing Directorate, Air Force Research Laboratory, 2977 Hobson Way, Wright-Patterson AFB, OH 45433-7126, USA.
Patterns (New York, N.Y.)
|October 3, 2025
概括
本综述探讨了在材料科学和制造业中用于机器学习 (ML) 的可解释的人工智能 (XAI). XAI提供了对复杂模型的洞察力,改善了预测,并通过小型而昂贵的数据集指导了未来的研究.
科学领域:
- 材料科学与工程 材料科学与工程
- 制造业 制造业 制造业 制造业
- 人工智能的人工智能
背景情况:
- 机器学习 (ML) 模型通常需要大量的数据集,由于成本高昂,在材料科学和制造业中很难获得这些数据集.
- 复杂的"黑子"ML模型缺乏透明度,导致预测中的潜在错误并阻碍信任.
- 机器学习模型的不透明性可能会导致效率低下或危险的结果,当数据处理或训练错误被忽视时.
研究的目的:
- 审查可解释的人工智能 (XAI) 方法,适用于资源有限的技术领域的ML.
- 展示XAI如何为ML模型行为提供关键洞察力.
- 引导XAI在材料科学和制造研究中的应用.
主要方法:
- 对现有可解释的人工智能 (XAI) 技术的文献综述.
- 分析XAI在应对小型数据集和昂贵数据生成方面的作用.
- 在材料科学和制造背景下XAI应用的说明性示例.
主要成果:
- 对于复杂的ML模型,XAI方法可以产生人类可解释的解释.
- 这些解释提供了可以防止低效或危险预测的见解.
- XAI可以为未来的研究方向提供信息,并改进功能工程.
结论:
- 可解释的人工智能 (XAI) 对于将机器学习 (ML) 适应于材料科学和制造业至关重要.
- XAI增强了理解,提高了预测准确度,并指导了未来的研究.
- 为在这些领域实施XAI提供了指导和示例.
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