Zhichao Wang

Zhichao Wang

Welcome to my personal website! I am a researcher focusing on the intersection of Deep Learning and Computational Fluid Dynamics (CFD), with a primary focus on Intelligence-driven Mesh Generation and Optimization.

My research explores how Graph Neural Networks (GNNs), Reinforcement Learning (RL), and unsupervised learning techniques can automate and accelerate critical workflows in grid-based physical simulations. My current research projects include:

  • Intelligent Mesh Movement & Smoothing: Developing generalizable mesh optimization models (e.g., UGM2N, GNNRL-Smoothing) that leverage unsupervised and prior-free learning for complex geometric variations.
  • Mesh Quality Evaluation: Constructing high-fidelity, intelligent indicators for mesh quality evaluation based on graph representations for unstructured and structured grids.
  • AI for Science (AI4S): Accelerating aerodynamic design optimization and exploring new paradigms for intelligence-driven CFD simulations.

Education

  • Ph.D. (expected) in Computer Science and Technology, National University of Defense Technology, 2023 - 2026
  • M.S. in Electronic Information, National University of Defense Technology, 2020 - 2022
  • B.S. in Mechatronic Engineering, Northwestern Polytechnical University, 2016 - 2020

Honors and Awards

  • University-level Outstanding Student, National University of Defense Technology, 2025
  • Second-class Academic Scholarship, National University of Defense Technology, 2025
  • College-level Outstanding Student, College of Computer, National University of Defense Technology, 2025
  • National Scholarship, Northwestern Polytechnical University, 2017

Publications

  • Ke YANG, Xinhai CHEN, Zhichao WANG, Qinglin WANG, Jie LIU. A Fine-Grained Intelligent Mesh Quality Evaluation Algorithm Based on Neural Networks [C]// 2026 International Joint Conference on Neural Networks (IJCNN). 2026. (Accepted Mar 24, 2026, CCF-C)
  • Pei J, Chen X, Liu Y, Wang Z, et al. Generalization-Aware Structured Mesh Smoothing via Graph Neural Networks[J]. Computers & Fluids, 2025: 106895. (CAS Q3, IF 3.0, Jan 30, 2026)
  • Xiao Q, Chen X, Wang Q, Guo X, Wang B, Chen W, Wang Z, et al. LLM4Fluid: Large Language Models as Generalizable Neural Solvers for Fluid Dynamics[J]. arXiv:2601.21681, 2026.
  • Wang, Z., Chen X, Gong C, et al. GNNRL-Smoothing: A prior-free reinforcement learning model for mesh optimization[J]. Neural Networks, 2025: 108235. (CAS Q2 Top, CCF-B, IF 6.3, Oct 19, 2025)
  • Wang Z, Chen X, Wang Q, et al. UGM2N: An unsupervised and generalizable mesh movement network via M-uniform loss[C]. In Advances in Neural Information Processing Systems (NeurIPS 2025). (CCF-A, Sep 18, 2025)
  • Chen X, Wang Z, Liu Y, et al. A neural network approach for unstructured mesh quality evaluation[J]. Engineering Computations, 2025, 42(6): 2021-2035. (CAS Q4, IF 1.9, Jul 25, 2025)
  • Wang Z, Chen X, Yan J, et al. An intelligent mesh-smoothing method with graph neural networks[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(3): 367-384. (CAS Q4, CCF-C, IF 2.9, Apr 7, 2025)
  • Wang Z, Chen X, Deng L, et al. A surface mesh smoothing method for aircraft based on unsupervised learning[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(10): 631172. (EI Indexed, Top Journal in Aeronautics, Feb 6, 2025)
  • Chen X, Wang Z, Deng L, et al. Towards a new paradigm in intelligence-driven computational fluid dynamics simulations[J]. Engineering Applications of Computational Fluid Mechanics, 2024, 18(1): 2407005. (CAS Q1 Top, IF 5.4, Sep 25, 2024)
  • Li T, Yan J, Chen X, Wang Z, et al. Accelerating aerodynamic design optimization based on graph convolutional neural network[J]. International Journal of Modern Physics C, 2024, 35(01): 2450007. (CAS Q4, IF 1.6)
  • Yan J, Chen X, Wang Z, et al. ST-PINN: A self-training physics-informed neural network for partial differential equations[C]// 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023: 1-8. (CCF-C, EI)
  • Chen X, Wang Z, Liu J, et al. A neural network-based mesh quality indicator for three-dimensional cylinder modelling[J]. Entropy, 2022, 24(9): 1245. (CAS Q3, IF 2.0, Sep 4, 2022)
  • Wang Z, Chen X, Li T, et al. Evaluating mesh quality with graph neural networks[J]. Engineering with Computers, 2022, 38(5): 4663-4673. (CAS Q2 Top, IF 4.9, Jul 30, 2022)

Academic Activities

  • Conferences:
    • Presentation: The 1st National Conference on Intelligent Fluid Mechanics (2024); The 2023 National Workshop on Mesh Generation and Applications (MEGAS 2023)
    • Attendance: The 2nd Chinese Conference of Aerodynamics (2023)
  • Journal Reviewer: Aerospace Science and Technology, Computer Methods in Applied Mechanics and Engineering, Engineering Applications of Artificial Intelligence, Neural Networks, Simulation Modelling Practice and Theory
  • Teaching Assistant: University Computing course, 2024 & 2025