主题号"基于物理的机器学习及其结构完整性应用"的序言
Shun-Peng Zhu1, Abílio M P De Jesus2, Filippo Berto3
1School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
概括
基于物理的机器学习通过将物理定律集成到模型中来增强结构完整性的评估. 这种方法提高了工程系统的概括性和可靠性,推进了材料科学和安全评估.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
- 物理 物理学 物理
背景情况:
- 机器学习 (ML) 在工程方面提供了巨大的潜力,但缺乏物理意义和通用性.
- 纯粹数据驱动的模型与新的场景和物理解释性作斗争.
- 将物理整合到机器学习中对于强大的工程应用至关重要.
研究的目的:
- 提供对基于物理的机器学习 (PIML) 的最新审查.
- 突出PIML在结构完整性和安全性评估中的应用.
- 探索材料科学中实时数据分析的高级ML算法.
主要方法:
- 将物理原理纳入机器学习算法.
- 开发复杂的ML技术用于数据分析.
- 专注于材料科学,疲劳和断裂力学.
主要成果:
- 皮姆 (PIML) 模型表现出更好的概括性和物理解释性.
- 在实时数据处理中提高准确性和生产力.
- 设计具有可靠安全评估的新材料和结构的潜力.
结论:
- 基于物理的机器学习是工程学的转型领域.
- PIML提高了ML在结构完整性的可靠性和适用性.
- 这项研究为未来材料设计和安全方面的进步铺平了道路.
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