Data Science
物理正则化合成数据生成
Physics-regularised synthetic data generationA hybrid framework for generating synthetic data that incorporates physical laws to improve the quality and reliability of data, specifically applied to predictive maintenance of filtration systems with small experimental datasets. / 一种混合框架，用于生成合成数据，该框架结合物理定律以提高数据质量和可靠性，特别是应用于具有小实验数据集的过滤系统预测性维护。
- Knowledge type
- 工艺技术
- First proposed or discovered
- Proposer
- Turkey Farm member
- Creator
- 农场主
- Last updated
- 2026-11-07 23:05:43
Sub-knowledge points
Predictive maintenanceFiltration systemsPhysics-informed machine learning
Higher-order knowledge points
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Main parameters and performance indicators
Filtration system physics: governing equations and constraintsSmall experimental dataset: limited real-world measurement dataHybrid framework: combination of physics-based and data-driven approaches
Open problems
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Related literature
ranked by community starsPhysics-regularised synthetic data generation for predictive maintenance of filtration systems: A hybrid framework for small experimental datasetsOpen source
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Creation and modification trail
1 recordsRecord title: Physics-regularised synthetic data generation.
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