1. 陈南，钟敏，许伯熹，带正则化项的时间序列聚类算法及其应用,复旦学报(自然科学版),51(2012),56-63.

2. Zhong M.,Lu S., Cheng J., Multiscale analysis for ill-posed problems with semi-discrete Tikhonov regularization, Inverse Problems, 28(6),2012,19-37.

3. Zhong M., Loy R. J., Anderssen R. S., Approximating the Kohlrausch function by sums of exponentials, ANZIAM J, 54(04), 2013, 19-37.

4. Jin Q., Zhong M.,On the iteratively regularized Gauss-Newton method in Banach spaces with applications to parameter identification problems, Numer.       Math., 124(4), 2013, 647-683.

5. Xu B., Lu S., Zhong M., Multiscale support vector regression method in Sobolev spaces  on bounded domains,  Applicable Analysis,  94(3), 2014, 1-22. 

6. Jin Q., Zhong M., Nonstationary iterated Tikhonov regularization in Banach spaces with general convex penalty term, Numer. Math., 127(3), 2014, 485-513. 

7. Hon Y.C., Schaback R., Zhong M., The meshless kernel-based method of lines for parabolic equations, Comput. Math. Appl. 68(12), 2014, 2057-2067.

8. Zhong M., Hon Y. C., Lu S., Multiscale support vector approach for solving ill-posed problems, J. Sci. Comput.64, 2015, 317-340.

9. Zhong M., Wang W., A global minimization algorithm for Tikhonov functionals with p- convex(p>=2)penalty  terms in Banach spaces,   Inverse Problems, 32, 2016, 104008 (30pp).

10. Zhong M., Liu J.J., On the reconstruction of media inhomogeneity by inverse wave  scattering model, Sci. China. Math., 60(10), 2017, 1825-1836.

11.Zhong M., Le Gia Q.T., Wang W., A multiscale support vector regression method on spheres with data compression, Applicable Analysis, 98(8),2019,  1496-1519.

12. Zhong M., Wang W., A regularizing multilevel approach for nonlinear inverse problems,  Appl. Numer. Math.,135, 2019, 297-315.

13. Zhong M., Jin Q., Wang W., Regularization of inverse problems by two-point gradient methods in Banach spaces., Numer.Math, 143(3), 2019, 713-747.

14.Zhong M., Wang W., The two-point gradient methods for nonlinear inverse problems based on Bregman projections., Inverse Problems, 2020,045012.

15. Shao, N., Zhong, M., Yan, Y., Pan, H. S., Cheng J., Chen W.B., Dynamic models for Coronavirus Disease 2019 and da.ta analysis., Mathematical Methods in the Applied Sciences, 43(7),2020:4943-4949.

16. Cheng J., Zhang J. T., Zhong M.  Extract the information from the big data from randomly distributed noise.  J. Inverse Ill-Posed Probl. 2021; 29(4): 525–541.

17. Zhong M., Wang W., Tong S. S. An asymptotical regularization with convex constraints for inverse problems. Inverse Problems, 2022 (38):  045007 (30pp).

18. Zhong M., Wang W., Zhu K.  On the asymptotical regularization with convex constraints for nonlinear ill-posed problems, Applied Mathematics Letters, 2022 (133): 108247.

19 Zhong M.,  LeGia Q.T., Sloan I.H.  A multiscale RBF method for severely ill-posed problems on spheres. J. Sci. Comput., 2023, 94:22.

20 Zhong M., Qiu L. Y., Wang W. Landweber-type method with uniformly convex constraints under conditional stability assumptions. Applied Mathematics Letters, 2023(144): 108723.

21    21 Chen Y., Cheng J., Zhang J. T., Zhong M. A big data processing technique based on Tikhonov regularization. Practical Inverse Problems and Their Prospects. vHiSilicon (Shanghai) Technologies CO.,LIMITED. Shanghai, China. 

         22  Hu Y., Zhong M.(corresponding author) Semi-discrete Tikhonov regularization in RKHS with large randomly distributed noise. Inverse Problems 2023 (39) : 095005 (23pp).

     23 Zhong M., Li X. Y.,  and Liu X. M. Extract the information via multiple repeated observations under randomly distributed noise. JIIP 2023. online https://doi.org/10.1515/jiip-2022-0063.

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