计算数学，人工智能，计算力学，计算流体力学，低阶模型（Reduced Order Modelling), 大数据，数据科学

2013.10-2016.10   帝国理工学院（Imperial College London）  博士

先后任职于帝国理工的地球科学系，数据科学所，英国斯旺西大学被誉为有限元发源地的 Zienkiewicz工程计算中心。现任职于同济大学。

Selected papers:

1. D. Xiao, F. Fang, C.C. Pain, I.M. Navon. A parameterized non-intrusive reduced order model and error analysis for general time-dependent nonlinear partial differential equations and its applications. Computer Methods in Applied Mechanics and Engineering, 2017, 317, 868-889.

2. D. Xiao, P. Yang, F. Fang, J. Xiang, C.C. Pain, I.M. Navon, Ming Chen. A non-intrusive reduced-order model for compressible fluid and fractured solid coupling and its application to blasting. Journal of Computational Physics. 2017, 330, 221-224.

3. D. Xiao. Error estimation of the parametric non-intrusive reduced order model using machine learning. Computer Methods in Applied Mechanics and Engineering. 2019, 355, 513-534.

4. D. Xiao, P Yang, F Fang, J Xiang, CC Pain, IM Navon. Non-intrusive reduced order modeling of fluid-structure interactions. Computer Methods in Applied Mechanics and Engineering. 2016, 303, 35-54.

5. D. Xiao, F. Fang, A.G. Buchan, C.C. Pain, I.M. Navon, J. Du, G. Hu. Non-Linear model reduction for the Navier-Stokes Equations using residual DEIM. Journal of Computational Physics. 263(2014), 1-18.  

6. D. Xiao, F. Fang, J. Du, C.C. Pain, I.M. Navon, A.H. ElSheikh, G. Hu. Non-Linear Petrov-Galerkin Methods for Reduced Order Modelling of the Navier-Stokes Equations using a Mixed Finite Element Pair. Computer Methods in Applied Mechanics and Engineering. 255 (2013),147-157.

7. D. Xiao, C.E. Heaney, L. Mottet, F. Fang, W. Lin, I.M. Navon, Y. Guo, O.K. Matar, A.G. Robbins，C.C. Pain, A Reduced Order Model for Turbulent Urban Flows Using Machine Learning, Building and Environment. 2019, 148, 323-337.

8. D. Xiao, F. Fang, J. Zheng, C.C. Pain, I.M. Navon, Machine learning-based rapid response tools for regional air pollution modelling. Atmospheric Environment. 2019, 199, 463-473.

9. 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, 390, 114532, 2022.

10. F. Fang, C. Pain, I.M. Navon, A.H. Elsheikh, J. Du, D. Xiao. Non-Linear Petrov-Galerkin Methods for Reduced Order Hyperbolic Equation and Discontinuous Finite Element Methods. Journal of Computational Physics. 234(2013) 540-559.

11. R Fu, D Xiao*, IM Navon, F. Fang L. Yang, S. Cheng, C Wang, A non-linear non-intrusive reduced order model of fluid flow by Auto-Encoder and self-attention deep learning methods, International Journal of Numerical Methods in Engineering. Accepted. 2023.

12. J Fu, D Xiao*, R Fu, C Li, C Zhu, R Arcucci, IM Navon， Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes， Computer Methods in Applied Mechanics and Engineering. 2023, 404, 115771.  

13. D Xiao, F Fang, AG Buchan, CC Pain, IM Navon, A Muggeridge， Non-intrusive reduced order modelling of the Navier–Stokes equations， Computer Methods in Applied Mechanics and Engineering 293, 522-541, 2015.

14. D Xiao, F Fang, CE Heaney, IM Navon, CC Pain, A domain decomposition method for the non-intrusive reduced order modelling of fluid flow, Computer Methods in Applied Mechanics and Engineering 354, 307-330, 2019.

主持（PI）过英国基金EPSRC项目（PURIFY), Royal Society 等基金项目以及国家级人才等项目。

肖敦辉，同济大学数学科学学院教授，入选国家海外高层次青年人才项目以及上海市海外高层次创新人才项目。 中国数学会计算数学分会第十一届常务理事。 担任多个期刊审稿人以及编委， 担任英国基金委EPSRC和卡塔尔基金委特邀审稿人。研究领域包括计算模型，计算力学, 数据驱动模型，物理与数据混合驱动计算模型、工业软件、数据科学、大数据、计算流体力学、人工智能。

高等数学D, 数值分析（英文），理科大类专业导论

特别欢迎有志于交叉学科方向研究（计算数学、 应用数学、机器学习、计算模型、计算流体力学、工业软件等）的学生或想从事博士后研究的多联系。

I am currently accepting PhDs and Post-docs in engineering, computational mechanics, computational or applied mathematics, machine learning, data sciences and computational fluid dynamics.

Please get in touch if you would like to discuss your research ideas further: 21174@tongji.edu.cn