1. Uncertainty quantification and computational statistics
2. Multiscale method and computation
3. Porous media application
Ph.D, Texas A&M University, 2008
2018/01-present School of Mathematical Sciences, Tongji University, Professor
2013/02-2017/12 Institute of Mathematics, Hunan University, Professor
2008/09-2010/08 IMA, University of Minnesota, Postdoctoral fellow
Some works in recent 5 years :
A multiscale virtual element method for elliptic problems in heterogeneous porous media, Journal of Computational Physics, 388 (2019), pp. 394-415. https://doi.org/10.1016/j.jcp.2019.03.031
A constraint energy minimizing generalized multiscale finite element method for parabolic equations, SIAM Multiscale Model. Simulation, 17 (2019), pp. 996-1018, https://arxiv.org/abs/1806.04816
A new bi-fidelity model reduction method for Bayesian inverse problems, International Journal for Numerical Methods in Engineering, 119 (2019), pp. 941-963 https://doi.org/10.1002/nme.6079
A reduced generalized multiscale basis method for parametrized groundwater flow problems in heterogeneous porous media, Water Resources Research, 15 (2019), pp 2390-2406. https://doi.org/10.1029/2018WR023954
- 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
- 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
- F. Chen, E. Chung, L. Jiang *, Adaptive least-squares generalized multiscale finite element method, SIAM Multiscale Model. Simulation, 16 (2018), pp. 1034--1058. http://doi.org/10.1137/17M1138844
- L. Jiang* and N. Ou, Bayesian inference using intermediate distribution based on coarse multiscale model for time fractional diffusion equation, SIAM Multiscale Model. Simulation, 16 ( 2018), pp. 327-355.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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, SIAM Multiscale Model. Simulation, 12 (2014), pp.119--151.
Granted projects in progress:
1. Stochastic multiscale model reduction and its applications, NSFC, PI.
2. Bayesian uncertainty quantification for random porous media models, NSFC, PI
●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 methods, statistical 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: email@example.com.