Dr. Nan DENG
Dr. Nan DENG
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Associate Professor | Supervisor of Master's Candidates
School of Robotics and Advanced Manufacture
Harbin Institute of Technology (Shenzhen)
Suite 911, Block G, University Town of Shenzhen, Nanshan District, 518055, Guangdong, China
dengnan@hit.edu.cn | nan.deng@ensta-paris.fr
Homepage:
(EN) Nan DENG - PERSONAL HOMEPAGE OF HIT
(CN) 邓楠 - 哈尔滨工业大学教师个人主页
I received my Ph.D. in Fluid Mechanics from Institut Polytechnique de Paris (IP Paris) in 2021. Immediately after, I returned to China and continued my academic career. I am currently an Associate Professor at Harbin Institute of Technology (Shenzhen).
My research focuses on automated reduced-order modeling (surrogate models) for digital twins, using flow data, machine learning, and first principles. These models support flow analysis, optimization, state estimation, and control. The starting point is mean-field modeling, sparse identification, Galerkin force modeling, and cluster-based modeling, which have been exemplified on the benchmark configuration, “Fluidic Pinball.”
We tap the great potential of ROM, synergizing machine learning techniques and first principles to meet the control and optimization requirements in complex industrial environments, addressing challenges of multi-scale and multi-frequency tasks.
Research interests:
Reduced-order modeling of complex systems using machine learning and first principles
Dimensionality reduction, feature extraction, and nonlinear modeling
Fluid dynamics, flow instabilities, and bifurcation theory
Numerical simulations of UAV aerodynamic shape
Join us!
We are looking for candidates in flow dynamics, nonlinear dynamics, CFD, machine learning, data science, and flow control. We also
Master students in Power Engineering and Engineering Thermophysics (General or CSC Program @ HIT Shenzhen)
PhD students in Power Engineering and Engineering Thermophysics (General or CSC Program @ HIT Shenzhen)
Postdoctoral researchers
The comprehensive annual income of post-doctoral fellows is no less than 300,000 RMB (Shenzhen Post-doctoral fellows' bonus included) before tax for the first two years.
[Qualifications may include the following:
- Ph.D. in Mechanics, Mathematics, Computer Science, or related area. Experience with dynamical modeling, data analysis, and AI is highly desirable.
- Doctoral degree obtained within 3 years, and under the age of 35. This condition can guarantee a tax-free bonus from the Shenzhen Municipal government in the amount of 180,000 RMB per year for 2 years.
- A proven track record in the form of publications in international English language journals, and the ability to communicate in written and spoken English.]
Professional Experience
2026. 01 - Now Associate Professor @ Harbin Institute of Technology (Shenzhen), China.
2024. 01 - 2026.01 Assistant Professor @ Harbin Institute of Technology (Shenzhen), China.
2022. 01 - 2024.01 Postdoctoral Fellow @ Harbin Institute of Technology (Shenzhen), China.
PI: Prof. Bernd R. Noack
Education Experience
2018. 10 - 2021.10 Ph.D. in Fluid Mechanics @ ENSTA-Paris, Institut Polytechnique de Paris, France
Thesis: Deep mean-field modeling for successive bifurcations exemplified for the fluidic pinball
Supervisors: Prof. Luc Pastur and Prof. Bernd R. Noack
Jury: Laurette S. Tuckerman (President), Dwight Barkley, Angelo Iollo, Steven L. Brunton, Lutz Lesshafft, Themistoklis Sapsis
Lab1: UME, IMSIA - Institut des Sciences de la Mécanique et Applications Industrielles
Lab2: AERO, LISN - Laboratoire Interdisciplinaire des Sciences du Numérique, CNRS
2016. 09 - 2018.09 M.Sc. in Fluid Mechanics @ University of Paris-Saclay, France
Master of Science: Fluid Dynamics and Energetics
Lab: AERO, LISN - Laboratoire Interdisciplinaire des Sciences du Numérique, CNRS
2015. 09 - 2016.08 B.Sc. in Physics @ University of Paris-Sud, France
Bachelor of Science: Physics and Applications
2012. 09 - 2015.08 B.Eng. in Optical Engineering @ Huazhong University of Science and Technology, China
Bachelor of Engineering: Optoelectronic Information Engineering
Reduced-order modelling and system identification can help us figure out the elementary degrees of freedom and the underlying mechanisms from the high-dimensional and nonlinear dynamics of fluid flow. Machine learning has brought new opportunities to these two processes and is revolutionising traditional methods. We focus on building a general framework to obtain a sparse human-interpretable model from complex high-dimensional data using machine learning and first principles.
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