通过基于大型语言模型的合成数据生成,加强在临床辅助水泥系统中对风湿性行为的分析
Murat Eser1, Yahya Kaya2, Ali Mardani2
1Department of Computer Engineering, Bursa Uludag University, Bursa 16059, Turkey.
Materials (Basel, Switzerland)
|August 14, 2025
概括
这项研究表明,大型语言模型 (LLM) 可以改善水泥和聚碳酸乙烯 (PCE) 混合物的兼容性预测. 通过LLM增强的模型准确地预测了水泥的形状,超过了传统方法.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 了解水泥和聚碳酸 (PCE) 添加剂的兼容性对于混凝土的性能至关重要.
- 研磨辅助剂 (GA) 影响水泥性能,它们与PCE添加剂的相互作用需要详细的研究.
研究的目的:
- 通过使用各种研磨辅助剂 (GA) 调查影响水泥和PCE添加剂兼容性的参数.
- 评估大型语言模型 (LLM) 对于合成数据增强对水泥风学预测建模的影响.
主要方法:
- 准备了29种水泥类型,其中7种含有四剂量的GA,以及87种含有三剂量的PCE的糊混合物.
- 使用赫歇尔-布克利模型评估了质行为 (动态收益应力和粘度).
- 使用卷积神经网络 (CNN),随机森林 (RF) 和神经分类和回归树 (NCART) 建模的数据,增强了LLM生成的合成数据.
主要成果:
- 在粘度和动态收益应力 (DYS) 方面,NCART显示出最好的基线性能.
- 基于LLM的合成数据增强显著提高了所有测试模型 (CNN,RF,NCART) 的预测准确度.
- 用GA生产的水泥比对照组显示出更高的DYS和粘度,这是由于颗粒尺寸分布更细.
结论:
- 基于LLM的合成数据增强显示了在水泥添加剂兼容性研究中增强预测模型的巨大潜力.
- 这些发现强调了GA在改变水泥性能方面的重要性,以及它们与PCE添加剂的相互作用.
- 这项研究开创了LLM用于材料科学建模中的合成数据增强的先驱性.
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