Director of the Foreign Exchange and Cooperation Office, School of Mathematics and Statistics, Northwestern Polytechnical University, China
Dr. Manyu Xiao, born on June 14, 1980, in Huangshi, Hubei, China, is a prominent associate professor at the School of Mathematics and Statistics, Northwestern Polytechnical University (NPU), Xi’an, China. Renowned for her extensive research in efficient numerical methods, model reduction, and parallel computing, Dr. Xiao has made significant contributions to mechanical optimization and surrogate-based topology optimization. She is highly regarded in the academic community for her innovative approaches and dedication to advancing computational mechanics.
Professional Profiles
Education
Dr. Xiao’s academic journey is marked by her pursuit of excellence across various prestigious institutions. She earned her Doctorate in Engineering Science from the Université de Technologie de Compiègne (UTC), France, in 2010, specializing in multidisciplinary optimization with model reduction and parallel computing. Prior to this, she completed her Master’s in Computational Mathematics at Northwestern Polytechnical University (NPU), China, in 2006, and her Bachelor’s in Mathematics and Applied Mathematics from Three Gorge University (TGU), China, in 2003.
Professional Experience
Dr. Xiao has held several significant positions throughout her career. Since March 2012, she has been serving as an associate professor at NPU. Her previous roles include a post-doctoral fellowship at UTC, France, and visiting scholar positions at renowned institutions such as the Université Libre de Bruxelles, University of Alabama in Huntsville, Queen Mary University of London, University of Oxford, University of Cambridge, and University of Helsinki. These roles have enabled her to collaborate on groundbreaking projects and enhance her pedagogical skills.
Research Interests
Dr. Xiao’s research interests are broad and impactful. She focuses on developing efficient numerical methods and exploring model reduction techniques and parallel computing. Her work in mechanical optimization and surrogate-based topology optimization is highly esteemed, contributing to advancements in computational mechanics and engineering applications.
Research Focuse
The research focus of M. Xiao centers on computational methods and optimization techniques in engineering, particularly in the context of structural design and analysis. Key areas of interest include topology optimization with applications to large-scale transient dynamic systems and stress-constrained environments. Xiao’s work also incorporates advanced modeling approaches such as on-the-fly reduced-order modeling and approximate reanalysis methods. Additionally, Xiao explores the integration of machine learning for classification and anomaly detection in large-scale building information modeling. Other notable contributions include the development of surrogate models for predicting dynamic behaviors in milling processes and the refinement techniques in structural optimization.
Publications
- Xiao, M., et al. “Primal-dual On-the-fly Reduced-Order Modeling for Large-Scale Transient Dynamic Topology Optimization,” Computer Methods in Applied Mechanics and Engineering,, Publication date: 2024.
- Xiao, M., et al. “Stress-constrained topology optimization using approximate reanalysis with on-the-fly reduced order modeling,” Advanced Modeling and Simulation in Engineering Sciences, Publication date: 2022.
- Xiao, M., et al. “Investigation of Classification and Anomalies based on Machine Learning Methods applied to Large Scale Building Information Modeling,” Applied Sciences, Publication date: 2022.
- Yang, Y., et al. “A Gaussian process regression-based surrogate model of the varying workpiece dynamics for chatter prediction in milling of thin-walled structures,” International Journal of Mechanical System Dynamics, Publication date: 2022.
- Xiao, M., et al. “Revisiting p-refinement in structural topology optimization,” Structures, Publication date: 2022.
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