| The method of fundamental solutions for the inverse heat source problem L Yan, CL Fu, FL Yang Engineering Analysis with Boundary Elements 32 (3), 216-222, 2008 | 245 | 2008 |
| Stochastic Collocation Algorithms using l1--Minimization L Yan, L Guo, D Xiu International Journal for Uncertainty Quantification 2 (3), 279-293, 2012 | 156 | 2012 |
| A meshless method for solving an inverse spacewise-dependent heat source problem L Yan, FL Yang, CL Fu Journal of Computational Physics 228 (1), 123-136, 2009 | 129 | 2009 |
| Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems L Yan, T Zhou Journal of Computational Physics 381, 110-128, 2019 | 120 | 2019 |
| Failure-informed adaptive sampling for PINNs Z Gao, L Yan, T Zhou SIAM Journal on Scientific Computing 45 (4), A1971-A1994, 2023 | 103 | 2023 |
| An adaptive surrogate modeling based on deep neural networks for large-scale Bayesian inverse problems L Yan, T Zhou Communications in Computational Physics 28, 2180-2205, 2020 | 63 | 2020 |
| Sparse Approximation using Minimization and Its Application to Stochastic Collocation L Yan, Y Shin, D Xiu SIAM Journal on Scientific Computing 39 (1), A229-A254, 2017 | 56 | 2017 |
| A computational method for identifying a spacewise‐dependent heat source L Yan, CL Fu, FF Dou International Journal for Numerical Methods in Biomedical Engineering 26 (5 …, 2010 | 52 | 2010 |
| Weighted approximate Fekete points: sampling for least-squares polynomial approximation L Guo, A Narayan, L Yan, T Zhou SIAM Journal on Scientific Computing 40 (1), A366-A387, 2018 | 36 | 2018 |
| Stochastic collocation algorithms using l1-minimization for Bayesian solution of inverse problems L Yan, L Guo. SIAM Journal on Scientific Computing 37 (3), A1410–A1435, 2015 | 35 | 2015 |
| The method of approximate particular solutions for the time-fractional diffusion equation with a non-local boundary condition L Yan, FL Yang. Computers and Mathematics with Applications 70 (3), 254-264, 2015 | 30 | 2015 |
| Convergence analysis of surrogate-based methods for Bayesian inverse problems L Yan, YX Zhang Inverse Problems 33 (12), 125001, 2017 | 29 | 2017 |
| On the uniqueness and reconstruction for an inverse problem of the fractional diffusion process JJ Liu, M Yamamoto, L Yan. Applied Numerical Mathematics 87, 1-19, 2015 | 29 | 2015 |
| The identification of a Robin coefficient by a conjugate gradient method F Yang, L Yan, T Wei International journal for numerical methods in engineering 78 (7), 800-816, 2009 | 29 | 2009 |
| A Bayesian inference approach to identify a Robin coefficient in one-dimensional parabolic problems L Yan, F Yang, C Fu Journal of Computational and Applied Mathematics 231 (2), 840-850, 2009 | 28 | 2009 |
| Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks Y Li, Y Wang, L Yan Journal of Computational Physics 475, 111841, 2023 | 25 | 2023 |
| Bayesian approach to a nonlinear inverse problem for time-space fractional diffusion equation YX Zhang, J Jia, L Yan Inverse Problems 34, 125002, 2018 | 25 | 2018 |
| Doubly stochastic radial basis function methods F Yang, L Yan, L Ling Journal of Computational Physics 363, 87-97, 2018 | 25 | 2018 |
| On the reconstruction of unknown time-dependent boundary sources for time fractional diffusion process by distributing measurement JJ Liu, M Yamamoto, L Yan Inverse Problems 32, 015009 (25pp), 2016 | 25 | 2016 |
| An adaptive multifidelity PC-based ensemble Kalman inversion for inverse problems L Yan, T Zhou International Journal for Uncertainty Quantification 9 (3), 205-220, 2019 | 23 | 2019 |
Tao ZhouProfessor of Mathematics, Chinese Academy of SciencesВерификована је имејл адреса на lsec.cc.ac.cn