Binghua Li, PhD in Aerospace Engineering and Fluid Mechanics, is currently an interim associate professor and master's supervisor at the Hangzhou International Innovation Institute of Beihang University.

His main research areas are data science (reduced-order models and modal analysis, flow field prediction, causal analysis) and turbulent flow control (turbulent numerical simulation, drag reduction mechanism analysis, and flow control technology), forming the distinctive research direction of AI4Science. He has published 8 SCI papers and 4 EI papers in journals such as the Journal of Computational Physics, Physics of Fluids, and Chinese Journal of Aeronautics, which have been cited over 100 times. He co-authored the Chinese academic monograph "Turbulent Flow Control and Drag Reduction Technology" as a core author.

He has led and participated in more than ten basic research and industry-funded projects, including Sino-European international cooperation projects, special projects from the Ministry of Industry and Information Technology, National Natural Science Foundation of China (NSFC) general projects, and youth projects. He has extensive overseas study and exchange experience and awarded the Outstanding Doctoral Graduate and the highest honor (Sobresaliente cum laude) from the Polytechnic University of Madrid. Currently, he servces as a member of the TAML Youth Editorial Board and a reviewer for journals such as PoF, OE, RESS, Acta Mechanica Sinica, and TNUAA.

We sincerely invite Master's and Doctoral students and postdoctoral fellows in aerospace, fluid mechanics, mechanical engineering, and information technology to join us (comprehensive annual salary of 470,000 RMB, with direct subsidies of 1.8 million RMB). Undergraduate students are also welcome to join and find guidance in competitions. For details, please refer to: ResearchGate Homepage, Team Homepage.

Main scientific publications：

[C4] Li, S.C., Zhang, L.L., Han, D.Y., Li, B.*, Wang H.W., & Kang, R. (2024). Capacity Prediction of Lithium-ion Batteries with Regeneration Phenomena Based on Hierarchical Modal Analysis. 2024 ICSRS, 54-60. (EI indexed) [DOI]

[J4] Li, B., Garicano-Mena, J.*, & Valero, E. (2022). A dynamic mode decomposition technique for the analysis of non–uniformly sampled flow data. Journal of Computational Physics, 468, 111495. (Top SCI) [DOI]

[J3] Li, B., Garicano-Mena, J.*, Zheng, Y., & Valero, E. (2020). Dynamic mode decomposition analysis of spatially agglomerated flow databases. Energies, 13(9), 2134. (SCI Q1) [DOI]

[C3] Li, B.*, Garicano-mena, J., Zheng, Y., Valero, E. (2020). Feature Extraction from Turbulent Channel Flow of Moderate Reynolds Number via Composite DMD Analysis. Journal of Physics: Conference Series, 1600, 012028. (EI indexed) [DOI]

[C2] Li, B.*, Garicano-Mena, J. & Valero, E. (2020). Feature Extraction from Turbulent Channel Flow Databases via Composite DMD Analysis. Journal of Physics: Conference Series, 1522, 012008. (EI indexed) [DOI]

[J2] Garicano-Mena, J., Li, B., Ferrer E., and Valero E. (2019). A composite dynamic mode decomposition analysis of turbulent channel flows. Physics of Fluids, 31, no. 1: 115102. (Top SCI) [DOI]

[C1] Li, B.*, Garicano-Mena, J., Ferrer E., and Valero, E. (2019). Feature Extraction Algorithms Applied to Turbulent Channel Flow Databases: The Effect of Agglomeration Strategies. 2019 EUCASS, 111. (EI indexed) [DOI]

[J1] Li, B.*, Huang, X. W., Zheng, Y., Xie, F. F., Wang, J., & Zou, J. F. (2018). Performance of flapping airfoil propulsion with LBM method and DMD analysis. Modern Physics Letters B, 32(12n13), 1840024. (SCI Q2) [DOI]