自动机器学习与基于人类知识的模型,基于财产的模型和疲劳问题相比
Enrique Castillo1, Alfonso Fernández Canteli2, Miguel Muñiz Calvente2
1University of Cantabria, Santander, Cantabria, Spain.
概括
人类知识对于开发可靠的基于属性的模型至关重要,特别是在疲劳分析中. 仅靠数据驱动的方法是不够的,需要混合方法,将人类专业知识与数据相结合,以便进行可靠的预测.
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
- 人工智能的人工智能
背景情况:
- 数据驱动的方法,如机器学习,面临疲劳分析的局限性,原因是数据不足和缺乏可解释性.
- 黑盒模型无法提供对疲劳现象的基本理解,并阻碍了超越实验数据的推断.
- 现有的疲劳模型 (S-N,GRV-N) 需要大量的数据,并且很难将压力比率 (R) 等关键参数纳入.
研究的目的:
- 强调基于人类的知识在科学建模中的重要性.
- 引入原始的基于属性的模型,保证非任意的参数解决方案.
- 为复杂的问题提出一种混合方法,将人类专业知识与数据驱动的参数估计相结合.
主要方法:
- 开发基于属性的模型,用确保满意度的方程和非任意参数来制定.
- 整合以人为基础的知识作为人工智能混合模型的核心组成部分.
- 在人为导向建模框架内应用数据驱动技术进行参数估计.
主要成果:
- 证明基于属性的模型克服了疲劳分析中纯数据驱动方法的局限性.
- 成功地将拟议的方法应用于疲劳S-N和GRV-N模型,确保理解和推断能力.
- 验证混合方法对疲劳之外的其他科学和工程问题的概括性.
结论:
- 基于人类的知识对于创建可靠,可解释和可概括的科学模型至关重要.
- 人工智能混合模型为材料疲劳等复杂现象提供了比纯数据驱动方法更好的替代方案.
- 提出的方法提供了一个框架,可以将基于属性的建模扩展到各种科学挑战,提高预测准确性和理解.
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