闫亮 - 东南大学 - 数学学院

个人简介

闫亮，教授、博士生导师。主要从事不确定性量化、贝叶斯建模与计算、科学机器学习以及偏微分方程反问题的研究。2017年入选江苏省高校“青蓝工程”优秀青年骨干教师培养对象，2018年入选东南大学首批“至善青年学者”（A层次）支持计划，2019年在第十一届反问题年会上获得“优秀青年学术奖”。2015年和2017年获得｀东南大学“吾爱吾师” 十大我最喜爱的老师‘称号，2019年获得`东南大学“吾爱吾师”-数学学院最受欢迎老师`称号。2021年获得东南大学首届“杰出教学奖”。主持国家自然科学基金重大研究计划(培育项目)一项、面上项目两项，主持完成国家自然科学基金青年项目和江苏省自然科学基金青年项目各一项。已经在SISC、IP、JCP、CMAME、SIAM JUQ等国内外刊物上发表40余篇学术论文。自从加入东南大学以来，承担数学学院本科生：数学分析、深度学习基础等课程；数学学院研究生：现代数值计算方法、不确定性量化方法导论；本科面上：高等数学（A）、复变函数；工科研究生：数值分析等课程教学任务。 2020-至今，东南大学博士研究生导师； 2017-至今，东南大学硕士研究生导师； 2011/6-至今，东南大学数学学院，讲师、副教授、教授 2006/9-2011/6，兰州大学数学与统计学院，应用数学专业，博士；2009/10-2011/3，美国普渡大学数学系，计算数学专业，联合培养博士；2002/9-2006/7，兰州大学数学与统计学院，数学基地班，学士

研究领域

不确定性量化、PDE反问题、贝叶斯建模及计算、科学机器学习－Uncertainty quantification －Inverse and ill-posed problems － Bayesian modeling and computation － Scientific m achine learning

近期论文

查看导师新发文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

1 . S. L. Wu, L. Yan, T. Zhou, Z. Zhou, Operator learning based coarse solver for Parareal , preprint , 2025. 2 . Y.Y. Wang, L.Yan, Data-driven operator inference for parameter estimation in nonlinear partial differential equation , J. Comput. Phy. , 544: 114442, 2026. 3 . Y. W. Yin, L.Yan, A novel direct imaging method for passive inverse obstacle scattering problem , Inverse Problems and Imaging , 20: 368-389, 2026. 4 . Y. W. Yin, L.Yan, TDDM: A transfer learning framework for physics-guided 3D obstacle scattering inversion , J. Comput. Phy. , 539: 114211, 2025. 5 . Y.Y. Wang, L.Yan, T. Zhou, Deep learning-enhanced reduced-order ensemble Kalman filter for efficient Bayesian data assimilation of parametric PDEs , Comput. Phys. Commun. , 311: 109544, 2025. 6. Y.W. Yin, L. Yan, Physics-aware deep learning framework for the limited aperture inverse obstacle scattering problem , SIAM J. Sci. Comput., 47(2):C313-C342, 2025. 7 . Z.W. Gao, L. Yan, T. Zhou, Adaptive operator learning for infinite-dimensional Bayesian inverse problems , SIAM/ASA J. Uncertainty Quantification, 12(4):1389-1423, 2024 . 8 . Y.W. Yin (尹运文), L. Yan, Bayesian model error method for the passive inverse scattering problem , Inverse Problem, 40: 065005, 2024 . 9 . H. Gu, X. Xu, L. Yan, Inverse elastic scattering by random periodic structures , J. Comput. Phy. , 501: 112785, 2024. 10. W.B. Liu , L. Yan, T. Zhou, Y.C. Zhou, Failure-informed adaptive sampling for PINNs, Part III: applications to inverse problems , CSIAM Trans. Appl. Math., 5(3):636-670, 2024 . 11 . Z.W. Gao , T. Tang, L. Yan, T. Zhou, Failure-informed adaptive sampling for PINNs, Part II: combining with re-sampling and subset simulation , Commun. Appl. Math. Comput. , 6: 1720-1741, 2024 (Invited contribution to a special issue for Prof. Remi Abgrall 's 61th birthday). 12 . Z.W. Gao(高志伟) , L. Yan, T. Zhou, Failure-informed adaptive sampling for PINNs , SIAM J. Sci. Comput., 45(4): A1971-A1994, 2023.( Highly Cited Paper，Hot Paper, code ) 13 . Y. Y. Wang(王艳艳), Q. Li(李倩), L.Ya n, Adaptive ensemble Kalman inversion with statistical linearization, C ommu n. Comput. Phy. , 33:1357-1380, 2023. 14 . Y.C. Li(李勇超),Y. Y. Wang (王艳艳) , L.Yan , Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks , J. Comput. Phy. , 475:111841, 2023. 15 . L. Yan, T. Zhou, Stein variational gradient descent with local approximations , Comput. Meth. Appl. Mech. Eng. , 386: 114087, 2021 . 16 . L. Yan, X.L. Zou(邹熙灵), Gradient-free Stein variational gradient descent with kernel approximation , Appl. Math. Letters , 121: 107465, 2021. 17 . L. Yan, T. Zhou, An acceleration strategy for randomize-then-optimize sampling via deep neural networks, J. Comput. Math. , 39(6):848-864, 2021. 18 . A. Narayan, L. Yan , T. Zhou. Optimal design for the kernel interpolation: applications to uncertainty quantification, J. Comput. Phy. , 430:110094, 2021 . 19 . L. Yan, T. Zhou, An a daptive surrogate modeling based on deep neural networks for large-scale Bayesian inverse problems , Commun. Comput. Phy., 28: 2180-2205, 2020. ( A special issue on Machine Learning for Scientific Computing ) 20 . F.L. Yang, L. Yan, A non-intrusive reduced basis EKI for time-fractional diffusion inverse problems , Acta Math. Appl.Sinica-English Serier, 36(1):183-202, 2020. ( A s pecial issue for IP ) 21 . L. Yan , T. Zhou. Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems, J. Comput. Phy. , 381: 110-128, 2019. 22 . L.Yan , T. Zhou. An adaptive multi-fidelity PC-based ensemble Kalman inversion for inverse problems, Int. J. Uncertainty Quantification , 9(3):205-220, 2019. 23 . Y.X. Zhang, J.X. Jian, L. Yan, Bayesian approach to a nonlinear inverse problem for time-space fractional diffusion equation , Inverse Problems , 34:125002(19pp), 2018. 24 . F.L. Yang, L. Yan , L. Ling. Doubly stochastic radial basis function me thods , J. Comput. Phy., 363: 87-97, 2018. 25 . L. Guo, A. Narayan, L. Yan , T. Zhou . Weighted approximate Fekete points: sampling for least-squares polynomial approximation , SIAM J. Sci. Comput., 40 (1), A366-A387, 2018. 26 . L. Yan , Y. X. Zhang. Convergence analysis of surrogate-based methods for Bayesian inverse problems , Inverse Problems , 33:125001(20pp), 2017. 27 . L. Guo, Y. Liu, L. Yan , Sparse recovery via lq-minimization for polynomial chaos expansions , Numer. Math. Theor. Meth. Appl., 10(4):775-797, 2017. 28. L. Yan , Y. Shin, D. Xiu. Sparse approximation using L1-L2 minimization and its application to stochastic collocation SIAM J. Sci. Comput., 39(1): A229–A254, 2017. 29. Y.X.Zhang, L. Yan . The general a posteriori truncation method and its application to radiogenic source identification for the Helium production-diffusion equation , Appl. Math. Model., 43 :126- 138, 2017. 30. J.J. Liu, M. Yamamoto, L. Yan . On the reconstruction of unknown boundary sources for time fractional diffusion process by nonlocal measurement . Inverse Problems, 32(1): 015009, 2016. 31. L. Yan , L. Guo . Stochastic collocation algorithms using l1-minimization for Bayesian solution of inverse problems, SIAM J. Sci. Comput., 37(3): A1410–A1435, 2015. 32. L. Yan , F. L. Yang. The method of approximate particular solutions for the time-fractional diffusion equation with a non-local boundary condition , Comput. Math. Appl., 70:254-264, 2015. 33. J.J.Liu, M. Yamamoto, L. Yan . On the uniqueness and reconstruction for an inverse problem of the fractional diffusion process , Appl. Numer. Math., 87:1-19, 2015. 34. L. Yan , F.L Yang. Efficient Kansa-type MFS algorithm for time-fractional inverse diffusion problems , Comput. Math. Appl., 2014, 67:1507-1520. 35. L. Yan , F.L. Yang. A Kansa-type MFS scheme for two-dimensional time fractional diffusion equations , Eng. Anal. Bound. Eleme., 2013, 37 (11): 1426–1435. 36. H. F. Zhao, L. Yan , J. J. Liu. On the interface identification of free boundary problem by method of fundamental solution. Numer. Linear Algebra Appl., 2013, 20 (2) ： 385-396. 37. L. Yan , L. Guo, D.Xiu. Stochastic collocation algorithms using L1-minimization , Int. J. Uncertainty Quantification , 2012, 2(3): 279–293 . (Highly Cited Paper ) 38. L. Yan , F. L. Yang, C. L. Fu. A new numerical method for the inverse source problems from a Bayesian statistical perspective. Int. J. Numer. Meth. Eng., 2011, 85:1460-1474 39. Y.X. Zhang, C. L. Fu, L. Yan . Approximate inverse method for stable analytic continuation in a strip domain. J. Comput. Appl. Math. , 2011, 235: 1979-1992 40. L. Yan , C. L. Fu, F. F. Dou. A computational method for identifying a spacewise-dependent heat source. Int. J. Numer. Meth. Biomedical Eng. , 2010,26: 597-608 41. L. Yan , F. L.Yang, C.L.Fu. A meshless method for solving an inverse spacewise-dependent heat source problem . J. Comput. Phy. , 2009, 228(1):123-136 42. F. L. Yang, L. Yan , T. Wei. The identification of a Robin coefficient by a conjugate gradient method. Int. J. Numer. Meth. Eng. , 2009,78:800-816 43. L. Yan , F. L. Yang, C. L. Fu. A Bayesian inference approach to identify a Robin coefficient in one-dimensional parabolic problems. J. Comput. Appl. Math. , 2009, 231(2):840-850 44. F. L. Yang, L. Yan , T. Wei. Reconstruction of part of a boundary for the Laplace equation by using a regularized method of fundamental solution. Inverse Problems Sci. Eng. , 2009,17(8):1113-1128. 45. F. L. Yang, L. Yan , T. Wei. Reconstruction of the corrosion boundary for the Laplace equation by using a boundary collocation method. Math. Comput. Simu. , 2009,79(7):2148-2156 46. L. Yan , C. L. Fu, F. L. Yang. The method of fundamental solutions for the inverse heat source problem. Eng. Anal. Bound. Elem. , 2008, 32(3) :216-222.

学术兼职

中国工业与应用数学学会(CSIAM)理事、CSIAM不确定性量化专委会委员、CSIAM反问题与成像专委会委员 Editorial Board ： Associate Editor: Numerical Mathematics: Theory, Methods and Applications , 2025.1- Editor Board: 数值计算与计算机应用 , 2024.1- 审稿人： SISC,SIAM-MMS, SIAM-JUQ, JCP, IP, CMAME, JSC, CiCP, IJNME, IJ4UQ, Neural Network, AMM, CMA, IPSE, JIIP, AML, AA, I.J. Heat Mass Trans., EABE, IEEE Systems, Man and Cybernetics: Systems; IEEE Signal Processing Letters; Knowledge-Based Systems; Neurocomputing ......