Dr. Ming Chen | Engineering | Research Excellence Award
Lecturer at Zhejiang Ocean University | China
Dr. Ming Chen is a researcher in composite structures, uncertainty quantification, and data-driven intelligent design, with a strong focus on underwater composite cylindrical shells. His work integrates numerical simulation, polynomial chaos expansion, Bayesian deep learning, symbolic regression, and automated machine learning for structural analysis, reliability assessment, and design optimization under uncertainty. He has published in leading journals including Mechanics of Advanced Materials and Structures and Journal of Marine Science and Engineering. According to Scopus, Dr. Ming Chen has 6 publications, 18 citations, and an h-index of 2. His research contributes to probabilistic machine learning frameworks, global sensitivity analysis, and digital-twin multi-fidelity modeling for advanced composite systems.
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Featured Publications
Data-driven approach for uncertainty quantification and risk analysis of composite cylindrical shells for underwater vehicles
– Mechanics of Advanced Materials and Structures
Sparse Polynomial Chaos Expansion for Uncertainty Quantification of Composite Cylindrical Shell with Geometrical and Material Uncertainty
– Journal of Marine Science and Engineering
Optimization of composite cylinder shell via a data-driven intelligent optimization algorithm
– Journal of Physics: Conference Series
Uncertainty quantification and global sensitivity analysis for composite cylindrical shell via data-driven polynomial chaos expansion
– Journal of Physics: Conference Series
Polynomial chaos expansion for uncertainty analysis and global sensitivity analysis
– Journal of Physics: Conference Series