基于物理的机器学习及其结构完整性应用:最先进的技术
Shun-Peng Zhu1, Lanyi Wang1, Changqi Luo1
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 (PIML) 通过将物理定律集成到模型中来增强结构完整性的评估. 这种方法提高了预测准确度,减少了数据依赖,克服了纯数据驱动方法的局限性.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 机器学习 (ML) 对结构完整性有希望,但面临诸如忽视物理定律和差异推断等挑战.
- 纯数据驱动的ML方法在关键组件分析中难以满足数据要求和概括性.
研究的目的:
- 审查将物理信息集成到机器学习模型中,创建基于物理的机器学习 (PIML).
- 探索PIML在结构完整性的应用,包括故障机制建模和预后和健康管理 (PHM).
主要方法:
- 讨论了将物理信息嵌入到ML算法中的各种技术.
- 审查在结构完整性领域的PIML应用现有文献.
主要成果:
- PIML显示了与先前知识增强一致性和提高外推性能的潜力.
- PIML提供了更好的预测准确性,可解释性和计算效率,同时减少了对广泛训练数据的依赖.
结论:
- 在结构完整性应用中,PIML与传统的ML相比具有显著的进步.
- 需要进一步的研究来开发先进的PIML,以确保可靠的工程系统完整性.
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