Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids
2019 · Chinese Journal of Aeronautics · 163 citations
https://doi.org/10.1016/j.cja.2019.05.001
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Chenzhou Xu
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Chenzhou Xu is an academic researcher from China National Space Administration. The author has contributed to research in topics: Advanced Multi-Objective Optimization Algorithms & Model Reduction and Neural Networks & Aerospace and Aviation Technology. The author has an h-index of 6, co-authored 8 publications.
ORCID: 0000-0001-8118-1541Papers by this author
worksHigh-dimensional aerodynamic data modeling using a machine learning method based on a convolutional neural network
2022 · Advances in Aerodynamics · 34 citations
https://doi.org/10.1186/s42774-022-00128-8
A risk-averse cooperative framework for neighboring energy hubs under joint carbon, heat and electricity trading market with P2G and renewables
2025 · Renewable Energy · 6 citations
https://doi.org/10.1016/j.renene.2025.123241
Enhancing surrogate-based aerodynamic shape optimization via dataset-independent generative modelling and dimensionality reduction
2026 · Engineering Applications of Computational Fluid Mechanics · 0 citations
https://doi.org/10.1080/19942060.2026.2629111
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