1. Zhaohui Fu, Tao Tang and Jiang Yang, Energy plus maximum bound preserving Runge–Kutta methods for the Allen–Cahn equation, J. Sci. Comput. 92 (2022), no. 3,Paper No. 97.

2. L. Li, X. Tai and J. Yang, Generalization Error Analysis of Neural Networks with the Gradient-Based Regularization, to appear on CiCP, 2022.

3. T. Tang, X. Wu and J. Yang, Arbitrarily high order and fully discrete extrapolated RK–SAV/DG schemes for phase-field gradient flows, J. Sci. Comput. 93 (2022), no. 2,Paper No. 38.

4. F. Li and J. Yang, A rigorously provable efficient monotonic-decaying scheme for shape optimization in Stokes flows with phase-field approaches, Comput. Methods Appl. Mech.Engrg. 398 (2022), Paper No. 115195, 24 pp.

5. Z. Fu and J. Yang, Energy plus strong stability preserving Runge–Kutta methods for the Allen–Cahn equation, J. Comput. Phys., 454(2022), pp. 110943.

6. T. Tang, B. Wang and J. Yang, Asymptotic analysis on the sharp interface limit of the time-fractional Cahn–Hilliard equation, to appear in SIAM J. App. Math., 82 (2022),no. 3, 773792..

7. J. Yang, Z. Yuan, and Z. Zhou, Arbitrarily High-order Maximum Bound Preserving Schemes with Cut-off Postprocessing for Allen-Cahn Equations, J. Sci. Comput., 90 (2022), no. 2, Paper No. 76, 36 pp.

8. Lili Ju, Xiao Li, Zhonghua Qiao and Jiang Yang, Maximum bound principle preserving integrating factor Runge–Kutta methods for semilinear evolution equations,J. Comput. Phys., 439 (2021), pp. 110405.

9. J. Yang and Q. Zhu, A Local Deep Learning Method for Solving High Order Partial Differential Equations, Numer. Math. Theory Methods Appl., 15 (2022), no. 1, 4267.

10. Lingfeng Li, Shousheng Luo, Xuecheng Tai and Jiang Yang, A new variational approach based on level-set function for convex hull problem with outliers, Inverse Probl.Imaging 15 (2021), no. 2, 315–338.

11. L. Li, S. Luo, X. Tai and J. Yang, A Level Set Representation Method for N-dimensional Convex Shape and Applications, Commun. Math. Res., 37 (2021), pp.180-208.

12. Buyang Li, Jiang Yang and Zhi Zhou, Arbitrarily high-order exponential cut-off methods for preserving maximum principle of parabolic equations, SIAM J. Sci. Comput.42 (2020), no. 6, A3957–A3978.

13. Q. Du, J. Yang and Z. Zhou, Time–Fractional Allen–Cahn equation: Analysis and Numerical Methods, J. Sci. Comput. 85 (2020), no. 2.

14. Chaoyu Quan, Tao Tang and Jiang Yang, How to Define Dissipation-Preserving Energy for Time-Fractional Phase-Field Equations, CSIAM Trans. Appl. Math., 1(3) (2020), 478-490.

15. T. Tang and J. Yang, Finding the maximal eigenpair for a large, dense, symmetric matrix based on Mufa Chen’s algorithm, Commun. Math. Res., 36 (2020), 93-112.

16. J. Shen, J. Xu, and J. Yang, A new class of efficient and robust energy stable schemes for gradient flows, SIAM Rev. 61-3 (2019), pp. 474-506.

17. L.F. Li, S. S. Luo, X.C. Tai and J. Yang, A Variational Convex Hull Algorithm, Seventh International Conference on Scale Space and Variational Methods in Computer Vision, 2019, organized by Microsoft Corporation. (Conference paper)

18. Q. Du, Y. Tao, X. Tian and J. Yang, Asymptotically compatible discretization of multidimensional nonlocal diffusion models and approximation of nonlocal Green’s functions, IMA J. Numer. Anal., 39(2) (2019), 607-625.

19. Tao Tang and Jiang Yang, Computing the Maximal Eigenpairs of Large Size Tridiagonal Matrices with O(1) Number of Iterations, Numer. Math. Theor. Meth. Appl.,11 (2018), pp. 877-894.

20. J. Shen, J. Xu, and J. Yang, The scalar auxiliary variable (SAV) approach for gradient flows, J. Comput. Phys., 353 (2018), 407-416.

21. Q. Du, J. Yang, and W. Zhang, Numerical analysis on the uniform L p -stability of Allen-Cahn equations, Int. J. Numer. Anal. Mod., 15(1-2) (2018), 213-227.

22. T. Hou, T. Tang and J. Yang, Numerical analysis of fully discretized Crank–Nicolson scheme for fractional-in-space Allen-Cahn equations, J. Sci. Comput., 72(3) (2017), 1214-1231.

23. Q. Du and J. Yang, Fast and Accurate Implementation of Fourier Spectral Approximations of Nonlocal Diffusion Operators and its Applications, J. Comput. Phys., 332(2017), 118-134.

25. W. Zhang, J. Yang, J. Zhang, and Q. Du, Artifificial boundary conditions for nonlocal heat equations on unbounded domain, Comm. Comp. Phys., 21(1) (2017), 16-39.

26. J. Shen, T. Tang and J. Yang, On the maximum principle preserving schemes for the generalized Allen-Cahn equation, Comm. Math. Sci., 14(6) (2016), 1517-1534.

27. T. Tang and J. Yang, Implicit-explicit scheme for the Allen-Cahn equation preserves the maximum principle, J. Comput. Math., 34(5) (2016), 471-481.

28. Q. Du, Y. Tao, X. Tian and J. Yang, Robust a posteriori stress analysis for approximations of nonlocal models via nonlocal gradients, Comp. Meth. Appl. Mech. Eng. 310(2016), 605-627.

29. Q. Du and J. Yang, Asymptotically compatible Fourier spectral approximations of nonlocal Allen-Cahn equations, SIAM J. Numer. Anal., 54(3) (2016), 1899-1919.

30. X. Feng, T. Tang and J. Yang, Long time numerical simulations for phase-fifield problems using p-adaptive spectral deferred correction methods, SIAM J. Sci. Comput. 37 (2015), A271-A294.

31. X. Feng, T. Tang and J. Yang, Stabilized Crank-Nicolson/Adams-Bashforth schemes for phase field models, East Asian Journal on Applied Mathematics, 3 (2013), pp. 59-80.