Jinlong Fu is a Full Professor in Computational Mechanics.
He completed his PhD in Computational Mechanics at Swansea University in 2020. Following his doctorate, he spent over 5 years in the UK, Germany, and other European countries, focusing on AI‑enhanced computational mechanics.
He is the recipient of the Alexander von Humboldt Fellowship from Germany, the Best PhD Thesis Award (the Roger Owen Prize), and the Annual Outstanding Contribution Award from the UK Association for Computational Mechanics (UKACM).
Intelligent Computational Mechanics Group: Our group focuses on the deep integration of AI and computational mechanics, namely Data‑driven Computational Mechanics and Physics‑aware Computational Mechanics. We are dedicated to developing accurate and efficient cross‑scale, multi‑physics intelligent computational methods and theoretical frameworks.
Prof. Jinlong Fu is a researcher and supervisor who is rigorous in scholarship, modest yet effective, and gentle and inclusive. The research group is harmonious, cohesive, well‑funded, and provides an excellent platform. We warmly welcome master's/PhD students and postdoctoral fellows to join us in exploring academic frontiers and building a brilliant future together.
There are ample openings for master's/PhD students and postdoctoral fellows in the next three years. Outstanding candidates from China or abroad are sincerely invited to apply. The group will provide each member with generous stipends, sufficient training funds, extensive opportunities for academic exchange, and resources for joint international training, supporting students to continuously break through and thrive on their research paths.
Email: jinlong.fu@dlut.edu.cn; jinlongfu@sina.cn
Representative Publications:
[1] Jinlong Fu and Wei Tan. Stochastic reconstruction of multiphase composite microstructures using statistics-encoded neural network for poro/micro-mechanical modelling. Computer Methods in Applied Mechanics and Engineering, 2025, 441:117986.
[2] Jinlong Fu, Dunhui Xiao, Rui Fu, Chenfeng Li, Chuanhua Zhu, Rossella Arcucci and Ionel M. Navon. Physics-data combined machine learning method for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes. Computer Methods in Applied Mechanics and Engineering, 2023, 404:115771.
[3] Jinlong Fu, Min Wang, Dunhui Xiao, Shan Zhong, Xiangyun Ge, Minglu Wu and Ben Evans. Hierarchical reconstruction of 3D well-connected porous media from 2D exemplars using statistics-informed neural network. Computer Methods in Applied Mechanics and Engineering, 2023, 410:116049.
[4] Jinlong Fu, Shaoqing Cui, Song Cen and Chenfeng Li. Statistical characterization and reconstruction of heterogeneous microstructures using deep neural network. Computer Methods in Applied Mechanics and Engineering, 2021, 373:113516.
[5] Jinlong Fu, Dunhui Xiao, Dongfeng Li, Hywel R. Thomas and Chenfeng Li. Stochastic reconstruction of 3D microstructures from 2D cross-sectional images using machine learning-based characterization. Computer Methods in Applied Mechanics and Engineering, 2022, 390:114532.
[6] Jinlong Fu, Hywel R. Thomas and Chenfeng Li. Tortuosity in Porous Media: Image Analysis and Physical Simulation. Earth-Science Reviews, 2021, 212:103439.
[7] Jinlong Fu, Min Wang, Bin Chen, Jinsheng Wang, Dunhui Xiao, Min Luo and Ben Evans. A data-driven framework for permeability prediction of natural porous rocks via microstructural characterization and pore-scale simulation. Engineering with Computers, 2023, 39(6): 3895-3926.
[8] Jinlong Fu, Wei Tan, Dunhui Xiao* and Xiaoying Zhuang*. Computational Intelligence in Stochastic Reconstruction of Porous Microstructures for Image-Based Poro/Micro-Mechanical Modeling. Archives of Computational Methods in Engineering, 2026, 33:433-501.
[9] Chuanhua Zhu, Dunhui Xiao, Jinlong Fu*, Yuntian Feng, Rui Fu and Jinsheng Wang. A data-driven computational framework for non-intrusive reduced-order modelling of turbulent flows passing around bridge piers. Ocean Engineering, 2024, 308: 118308.
[10] Min Luo, Siqi Zhong, Jiaxin Wu and Jinlong Fu*. A hybrid Conv-LSTM network with skip connections for nonlinear reduced-order modeling of spatiotemporal flow fields. Ocean Engineering, 2026, 347: 124033.