师资队伍

基本信息

姓名:姜立建

部门:计算数学

职称:教授

E-mail:ljjiang@tongji.edu.cn

研究方向:

   1.  Uncertainty quantification and computational statistics 

   2.  Multiscale method and computation

   3.  Porous media application 

教育背景:

Ph.D,  Texas A&M University, 2008

工作经历:

2018/01-present   School of  Mathematical Sciences,  Tongji University,  Professor 

2013/02-2017/12   Institute of Mathematics,   Hunan University, Professor 

2010/09-2013/01  LANL, Research fellow

2008/09-2010/08  IMA,  University of Minnesota,  Postdoctoral fellow 

论文与出版物:

Representative works in recent 5 years (* denotes corresponding author):

  1. Y. Ba, L. Jiang * and N. Ou,   A two-stage ensemble Kalman filter based on multiscale model reduction for inverse   problems in time fractional diffusion-wave equations,  Journal of Computational Physics, 374 (2018), pp. 300-330.   https://doi.org/10.1016/j.jcp.2018.06.077
  2.  Lingling Ma, Qiuqi Li and L. Jiang *, Local-global model reduction method for stochastic optimal control problems constrained by partial differential equations,  Computer Methods in Applied Mechanics and Engineering,  339 (2018), pp. 514-541.        http://doi.org/10.1016/j.cma.2018.05.012
  3. F. Chen, E. Chung, L. Jiang *, Adaptive least-squares generalized multiscale finite element method,  Multiscale Modeling and Simulation, 16 (2018), pp. 1034--1058. http://doi.org/10.1137/17M1138844
  4. L. Jiang* and N. Ou, Bayesian inference using intermediate distribution based on coarse multiscale model for time fractional diffusion equation,  Multiscale Modeling and Simulation, 16 ( 2018), pp. 327-355.
  5. L. Jiang* and Q. Li, Model reduction method using variable-separation for stochastic saddle point problems, Journal of Computational Physics, 354 (2018), pp. 43-66.
  6. Q. Li and L. Jiang*, A novel variable-separation method based on sparse representation for stochastic partial differential equations, SIAM Journal on Scientific Computing, 39 (2017), pp. A2879-2910.
  7. L. Jiang* and Q. Li, Model's sparse representation based on reduced mixed GMsFE basis methods, Journal of Computational Physics, 38 (2017), pp. 285--312.
  8. L. Jiang* and N. Ou, Multiscale model reduction method for Bayesian inverse problems of subsurface flow, Journal of Computational and Applied Mathematics, 319 (2017), pp. 188-209.
  9. F. Chen, E. Chung and L. Jiang*, Least-squares mixed generalized multiscale finite element method, Computer methods in applied mechanics and engineering, 311 (2016), pp. 764--787.
  10. L. Jiang* and X. Li, Multi-element least square HDMR methods and their applications for stochastic multiscale model reductions, Journal of Computational Physics, 294 (2015) , pp 439--461
  11. L. Jiang*, D. Moulton and J. Wei, A hybrid HDMR for stochastic mixed multiscale finite element method with application for flow in random porous media, Multiscale Modeling and Simulation, 12 (2014), pp.119--151.
  12. X. He, L. Jiang*, and D. Moulton, A stochastic dimension reduction multiscale finite element method for groundwater flow problems in heterogeneous random porous media, Journal of Hydrology, 478 (2013), pp. 77--88.

Some recent preprints

   1.  Bayesian identification of discontinuous fields with an ensemble-based variable separation multiscale method 

      http://arxiv.org/abs/1809.07994

   2.  A constraint energy minimizing generalized multiscale finite element method for parabolic equations

        https://arxiv.org/abs/1806.04816 

   3.  A new bi-fidelity model reduction method for Bayesian inverse problems.

   4. A multiscale virtual element method for elliptic problems in heterogeneous porous  media. 

   5. An improved implicit sampling for Bayesian inverse problems of multi-term time                                 fractional  multiscale diffusion models   https://arxiv.org/abs/1811.10189

    6. Ensemble-based implicit sampling for Bayesian inverse problems with non-Gaussian  priors                   http://arxiv.org/abs/1812.00375

 

科研项目:

Granted projects in progress:

1. Stochastic multiscale model reduction and its applications,  CNSF, PI.

2. Bayesian uncertainty quantification for random porous media models, CNSF, PI

Editorial Service

    ●Associate Editor: Journal of Computational and Applied Mathematics

    ●Associate Editor: Journal on Numerical Methods and Computer Applications (数值计算与计算机应用)

 

I am looking for graduate students (master and Ph.D) to join  our research group. Senior undergraduate students are also welcome. Our research attempts to develop, analyze and implement novel numerical   methodsstatistical methods and machine learning  for multiscale models and stochastic models, and investigates their applications in applied sciences and engineering. Interdisciplinary research is our preference. Research assistanceship is provided. For further information, please contact me via email: ljjiang@tongji.edu.cn.
 

 特别欢迎有志于交叉学科(数学、统计和机器学习、计算科学和油气水资源建模等)研究的学生联系我。
 

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