Transfer Learning and Data Augmentation in Engineering: Bridging Gaps for Smart Industrial Solutions
A special issue of Eng (ISSN 2673-4117).
Deadline for manuscript submissions: 31 July 2026 | Viewed by 24
Special Issue Editor
Special Issue Information
Dear Colleagues,
Recent advances in artificial intelligence and machine learning could revolutionize modern engineering applications. However, the effectiveness of smart industrial solutions often faces practical challenges due to limited volumes labeled data, domain shifts, and varying operational conditions. Transfer learning and data augmentation have emerged as powerful paradigms via which to address these limitations by leveraging knowledge from related domains and generating high-quality training data to enhance the generalization of models.
This Special Issue aims to gather high-quality research that explores innovative methods, theories, and applications related to transfer learning and data augmentation in various engineering contexts. We welcome contributions that advance fundamental methodologies, propose novel architectures or frameworks, and demonstrate real-world deployments in industrial scenarios such as manufacturing, fault diagnosis, predictive maintenance, robotics, autonomous systems, and more.
We welcome the submission of both theoretical and empirical studies that address domain adaptation, cross-domain feature alignment, synthetic data generation, weakly supervised learning, and applications involving edge computing or real-time industrial AI systems. By bridging the gap between data scarcity and intelligent solutions, this Special Issue seeks to foster interdisciplinary insights that promote robust, scalable, and trustworthy engineering systems.
We invite researchers and practitioners from academia and industry to submit original research articles, comprehensive reviews, and case studies that inspire future innovations at the intersection of transfer learning, data augmentation, and engineering applications.
Dr. Mengzhu Wang
Guest Editor
Manuscript Submission Information
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Keywords
- transfer learning
- domain adaptation
- data augmentation
- smart manufacturing
- fault diagnosis
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