Transfer Learning and Data Augmentation in Engineering: Bridging Gaps for Smart Industrial Solutions

Dear Colleagues,

Recent advances in artificial intelligence and machine learning hold great potential to revolutionize engineering applications, yet practical challenges, such as limited labeled data, domain shifts, and varying operational conditions, often hinder the effectiveness of smart industrial solutions. Transfer learning and data augmentation have emerged as key paradigms to overcome these limitations, enabling models to leverage knowledge from related domains and benefit from synthetically enhanced training data.

This Special Issue invites high-quality research on innovative methods, theories, and applications of transfer learning and data augmentation across engineering fields. Contributions may address fundamental methodologies, novel architectures, or real-world deployments in areas such as manufacturing, fault diagnosis, predictive maintenance, robotics, and autonomous systems. Topics of interest include domain adaptation, cross-domain feature alignment, synthetic data generation, weakly supervised learning, and edge computing or real-time AI systems in industrial settings.

By bridging the gap between data scarcity and intelligent solutions, this Special Issue aims to foster interdisciplinary insights that advance robust, scalable, and trustworthy engineering systems. We welcome original research, comprehensive reviews, and case studies from researchers and practitioners in academia and industry, inspiring future innovations at the intersection of transfer learning, data augmentation, and engineering.

Dr. Mengzhu Wang
Guest Editor

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