Dr. Wang Jia | Engineering | Women Researcher Award
Dr. Wang Jia | Engineering – Student at Shanghai Jiao Tong University, China
Wang Jia is an emerging scholar in the field of computational fluid dynamics and artificial intelligence, currently pursuing her Ph.D. in Transportation Engineering. Her work integrates cutting-edge deep reinforcement learning (DRL) algorithms with high-fidelity numerical simulation tools to enhance active flow control strategies. With a multidisciplinary foundation in hydraulic engineering, computer science, and high-performance computing, she is known for her innovative contributions in simulating and optimizing fluid behavior around complex geometries. Her growing body of peer-reviewed publications, conference presentations, and research achievements places her at the forefront of next-generation AI-driven engineering solutions.
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ORCID | Google Scholar
Education:
Wang Jia’s academic journey reflects a track record of excellence across all levels. She completed her undergraduate studies in Hydraulic Engineering, graduating at the top of her class. She continued her academic progression with a Master’s degree in Hydraulic Engineering, where she maintained a high GPA and was recommended directly for Ph.D. studies. Currently, she is a Ph.D. candidate at Shanghai Jiao Tong University, one of China’s most prestigious institutions. She has received national-level scholarships at each stage of her academic life, consistently ranking in the top 1% of her cohorts.
Experience:
Wang Jia has built substantial experience in simulation-driven research, combining physics-based models with data-driven intelligence. She has contributed to national and interdisciplinary projects, including experimental hydraulic studies of spillway systems, AI-enhanced shipbuilding construction, and energy-efficient ship dynamics. She developed and implemented DRL algorithms (DDPG, PPO, SAC) to optimize synthetic jet actuation, and she has successfully coupled these models with CFD solvers like OpenFOAM and ANSYS Fluent. Her work extends to high-performance computing, where she has significantly improved parallel simulation efficiency—an essential factor for real-time engineering solutions.
Research Interests:
Her primary research interests include deep reinforcement learning for flow control, high-performance computing in fluid dynamics, and intelligent systems for energy-efficient engineering. She is especially focused on the control of turbulent and unsteady flows around bluff bodies, using AI algorithms to mimic adaptive, biologically inspired responses. Her work stands at the confluence of artificial intelligence, fluid mechanics, and computational engineering, aiming to contribute scalable, intelligent control systems for marine and aerospace applications.
Awards:
Throughout her academic career, Wang Jia has consistently earned prestigious scholarships and honors that recognize both academic excellence and research potential. She received the National Scholarship at the undergraduate, master’s, and doctoral levels—a rare feat. She was also awarded an “Outstanding Oral Presentation” at a national Ph.D. forum and was selected to present at high-profile academic conferences such as ASME’s International Offshore Engineering event. These honors affirm both the quality of her research and her ability to communicate it effectively within the scientific community.
Selected Publications 
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Robust and Adaptive Deep Reinforcement Learning for Enhancing Flow Control around a Square Cylinder, Physics of Fluids, 2024 — Cited by: 11 -
Deep Reinforcement Learning-Based Active Flow Control of an Elliptical Cylinder, Physics of Fluids, 2024 — Cited by: 8 -
Optimal Parallelization Strategies for Active Flow Control in DRL-Based CFD, Physics of Fluids (Featured Article), 2024 — Cited by: 8 -
Effect of Synthetic Jets Actuator Parameters on DRL-Based Flow Control, Physics of Fluids (Special Topic), 2024 — Cited by: 6 -
Fluctuating Characteristics of the Stilling Basin with a Negative Step, Water, 2021 — Cited by: 5 -
Time-Frequency Characteristics of Fluctuating Pressure Using HHT, Mathematical Problems in Engineering, 2021 — Cited by: 1 -
Strategies for Energy-Efficient Flow Control Leveraging DRL, Engineering Applications of Artificial Intelligence, 2025 — Published, citations pending
Conclusion:
Wang Jia represents a new generation of researchers equipped with the computational tools, engineering insight, and intellectual rigor to solve complex problems at the intersection of AI and fluid dynamics. Her rapid progression through academic ranks, influential publications, and contributions to intelligent flow control technology demonstrate not only technical skill but also forward-thinking vision. She is especially deserving of recognition through the Women Researcher Award for her excellence in STEM, commitment to innovation, and strong potential for future impact in science and engineering.
A decomposition-based multiobjective evolutionary algorithm with weight vector adaptation – Swarm and Evolutionary Computation, 2021. Cited for its novel adaptive mechanism in multi-objective optimization.
An approach for solving the three-objective arc welding robot path planning problem – Engineering Optimization, 2023. Frequently referenced in robotics and optimization studies.
Online obstacle avoidance path planning and application for arc welding robot – Robotics and Computer-Integrated Manufacturing, 2022. Cited in real-time control literature.
A Collision-free path planning approach based on rule-guided lazy-PRM with repulsion field for gantry welding robots – Robotics and Autonomous Systems, 2024. Recent paper gaining citations in dynamic path planning.
Multi-objective task sequencing and trajectory planning under dynamic constraints – Manuscript in progress for Journal of Intelligent Manufacturing.
Authors: Kenzhaliyev, B., Mukhanova, A., Surkova, T., Amanzholova, L., Baltabekova, Z.
Year: 2024
Citations: 0
