Jichao Li
Northwestern Polytechnical University, Institute of High Performance Computing
0000-0003-2541-2557
About
My research is to improve the practicality of aerodynamic shape optimization in the industrial design of aircraft. I aim to provide effective solutions to large-scale aerodynamic shape optimization problems, especially those with massive design points, uncertain design variables, discontinuous merit functions, and multiple design objectives. The difficulties in addressing the demands come from two fundamental issues: the high dimensionality of shape design variables and the high computational cost of CFD simulations. My research shows that scientific machine learning can solve these issues and realize practical and large-scale aerodynamic shape optimization for the industry.
Employment
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Northwestern Polytechnical University Professor2024 - Present
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Institute of High Performance Computing Scientist2022 - 2024
Education
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Northwestern Polytechnical University PhD2013 - 2019
Projects & Funding
Projects & funding information is unavailable.
Publications (26)
- Generalizable Multifidelity Aerodynamic Wing Shape Design Optimization Save
- Aerodynamic shape optimization with effective deep-learning-based geometric filtering: from a cylinder to a wing Save
- Aerodynamic shape optimization of hypersonic aircraft using data-driven generative nonlinear parameterization Save
- Operation-Aware Aircraft Wing Design Using Cluster-Based Multipoint Aerodynamic Shape Optimization Save
- Data-driven modal parameterization for robust aerodynamic shape optimization of wind turbine blades Save
- Machine learning in aerodynamic shape optimization Save
- Reinforcement-learning-based control of confined cylinder wakes with stability analyses Save
- Low-Reynolds-number airfoil design optimization using deep-learning-based tailored airfoil modes Save
- Machine Learning in Aerodynamic Shape Optimization Save
- Physics-Based Data-Driven Buffet-Onset Constraint for Aerodynamic Shape Optimization Save