2009.9-2014.6 复旦大学数学科学学院 博士。
2011.9-2012.9 澳大利亚联邦科工组织(CSIRO,commonwealth science and industury research organization)CMIS 国家公派联合培养。
2005.9-2009.6 兰州大学数学与统计学院基地班 学士
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.
Z
hong
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 s
upport 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., 2022, 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.
