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About

Zewen Hu is pioneering the intersection of physics-informed modeling and machine learning to revolutionize surface roughness prediction in manufacturing. His research focuses on developing hybrid approaches that combine theoretical process knowledge with advanced data-driven techniques, addressing the critical challenge of quality control under real-world constraints like limited data and varying working conditions. Hu’s most impactful work introduces a **physics-guided meta-learning framework** (2025, 4 citations) that enables accurate surface roughness modeling even with sparse datasets, overcoming a major limitation of conventional methods. He further advances the field with a **knowledge-based fuzzy broad learning system** (2025, 3 citations) that integrates error correction for grinding processes, and a **cascade of theoretical models with regularized extreme learning machines** (2025, 3 citations) that balances precision and efficiency. These contributions directly tackle the time-consuming and costly nature of traditional manual inspection, offering manufacturers a path toward automated, data-informed decision-making. By bridging the gap between fundamental physics and modern AI, Hu is establishing a new paradigm for process optimization in precision manufacturing.