バイオグラフィー
Debiao Meng is an associate professor in School of Mechanical and Electrical Engineering in University of Electronic Science and Technology of China, China. He is also a Visiting Professors in the University of Bologna, and a Visiting Research Fellow at Zigong Innovation Centre of Zhejiang University. He obtained his degree BSc (2007) in Mechanical Engineering from Northwest A&F University and Ph.D. degree from the School of Mechanical and Electrical Engineering, UESTC with a major in Mechanical Engineering (2014). From 2013 to 2015, he was a joint Ph.D. student in the Department of Civil and Environmental Engineering at Vanderbilt University, US; and from 2017 to 2019, he was a Visiting Scholar at Rutgers University, US. His main research field is uncertainty-based multidisciplinary design and optimization of complex engineering systems. He has published more than 40 journal and proceedings papers on system reliability analysis, multidisciplinary design optimization, surrogate modeling, and uncertainty quantification and propagation.
研究の興味
Structural integrity; Structural strength; Reliability engineering; Design and optimization; Uncertainty analysis; Renewable energy; Wind power equipment; Intelligent algorithm; Multidisciplinary design optimization; Multiphysics coupling analysis.
Editor
仕事内容
Associate professor
University of Electronic Science and Technology of China
School of Mechanical and Electrical Engineering
China
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研究論文
- EB Naevi-like Lesion in Infant Bullous Pemphigoid
- A Study to Determine the Reason for Lower Pregnancy Rates in Younger Women with Diminished Oocyte Reserve-less Chance of Implanting vs. Fetal Demise
- The Role of Supplementation in Enhancing Recovery and Endurance among Fitness Trainers
- Homologous Series of Chemical Compounds in Three-component Systems (Aa+ – Bb+ – Cc–) and (Zn2+ - Ge4+ - P3-) in Generalized Form
- Problem of Surface Waves on Water in Higher School Laboratory Workshop
- Analytical Expressions of the Markov Chain of K-Ras4B Protein within the Catalytic Environment and a New Markov-State Model
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