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Updated: Jan 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Published on: December 6, 2024

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学习潜伏硬化:通过对物质反向问题的领域知识来增强深度学习

Qinyi Tian1, Winston Lindqwister2, Manolis Veveakis1

  • 1Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
|September 25, 2025
PubMed
概括

将域名知识纳入深度学习模型显著改善了它们在数据稀缺材料科学反向问题的预测性能. 这种方法增强了特征选择,并认识到材料行为和微观结构之间的关键联系.

关键词:
深度学习是一种深度学习.反向问题反向问题机器学习是机器学习.微观结构就是微观结构.有孔的材料是多孔的材料.

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