[49] Chen D., Li J., Zhang Y.*, A posterior contraction for Bayesian inverse problems in Banach spaces. Inverse Problems. 2024. To appear, DOI:10.1088/1361-6420/ad2a03.
[48] Wang Y., Huang Q., Yao Z., Zhang Y.*, On a class of linear regression methods, Journal of Complexity, 2024, 82, 101826.
[47] Zhang Y., Chen C., Stochastic linear regularization methods: random discrepancy principle and applications, Inverse Problems, 2024, 40, 025007.
[46] K. Zhu, Z. Shen, M. Wang, L. Jiang, Y. Zhang, T. Yang, H. Zhang, M. Zhang. Visual Knowledge Domain of Artificial Intelligence in Computed Tomography: A Review Based on Bibliometric Analysis. Journal of Computer Assisted Tomography. 2024. To appear, DOI:10.1097/RCT.0000000000001585.
[45] Chaikovskii D., Zhang Y.*, Solving forward and inverse problems involving a nonlinear three-dimensional partial differential equation via asymptotic expansions. IMA Journal of Applied Mathematics, 2023, 88, 525-557.
[44] Chaikovskii D., Liubavin A., Zhang Y.*, Asymptotic expansion regularization for inverse source problems in two-dimensional singularly perturbed nonlinear parabolic PDEs. CSIAM Transactions on Applied Mathematics, 2023,4(4), 721-757.
[43] Huang Q., Gong R., Jin Q., Zhang Y., A Tikhonov regularization method for Cauchy problem based on a new relaxation model. Nonlinear Analysis: Real World Applications, 2023, 74, 103935.
[42] Ran Q., Cheng X., Gong R., Zhang Y., A dynamical method for optimal control of the obstacle problem. Journal of Inverse and Ill-Posed Problems, 2023; 31(4): 577–594.
[41] Su J., Yao Z., Li C., Zhang Y., A Statistical Approach of Estimating Adsorption Isotherm Parameters in Gradient Elution Preparative Liquid Chromatography. Annals of Applied Statistics, 2023, 17(4), 3476-3499.
[40] Shcheglov A., Li J., Wang C., Ilin A., Zhang Y., Reconstructing the Absorption Function in a Quasi-Linear Sorption Dynamic Model via an Iterative Regularizing Algorithm, Advances in Applied Mathematics and Mechanics, 2023, 16(1), 1-16.
[39] Gong R., Wang M., Huang Q., Zhang Y., A CCBM-based generalized GKB iterative regularization algorithm for inverse Cauchy problems. Journal of Computational and Applied Mathematics, 2023, 432(1), 115282.
[38] Chen D., Li J., Zhang Y., Convergence rates of stationary and non-stationary asymptotical regularization methods for statistical inverse problems in Banach spaces. Communications on Analysis and Computation, 2023, 1, 32-55.
[37] Zhang Y., On the acceleration of optimal regularization algorithms for linear ill-posed inverse problems. Calcolo, 2023, 60, 1, Article number: 6.
[36] Zhang Y., Chen C.. Stochastic asymptotical regularization for linear inverse problems. Inverse Problems, 2023, 39, 015007.
[35] Lysak T., Zakharova I., Kalinovich A., Zhang Y., Two-color self-similar laser beams in active periodic structures with Pt-symmetry and quadratic nonlinearity, AIP Conf. Proc., 2023, 2872, 060003.
[34] Abramyan M., Melnikov B., Zhang Y., Some more on restoring distance matrices between DNA chains: reliability coefficients. Cybernetics and Physics, 2023, 12(4), 237–251.
[33] Melnikov B., Zhang Y., Chaikovskii D.. An Algorithm for the Inverse Problem of Matrix Processing: DNA Chains, Their Distance Matrices and Reconstructing, Journal of Biosciences and Medicines, 2023, 11, 310-320.
[32] Chaikovskii D., Zhang Y.*. Convergence analysis for forward and inverse problems in singularly perturbed time-dependent reaction-advection-diffusion equations. Journal of Computational Physics, 2022, 470, 111609.
[31] Hu B., Qian K., Zhang Y., Shen J., Schuller B, The Inverse Problems for Computational Psychophysiology: Opinions and Insights. Cyborg and Bionic Systems, 2022, 2022, 9850248.
[30] Melnikov B., Zhang Y., Chaikovskii D., An inverse problem for matrix processing: an improved algorithm for restoring the distance matrix for DNA chains. Cybernetics and Physics, 2022, 11(4), 217–226.
[29] Yang J., Xu C., Zhang Y.. Reconstruction of the S-Wave Velocity via Mixture Density Networks With a New Rayleigh Wave Dispersion Function. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60, 035004.
[28] Xu C., Zhang Y.*. Estimating the memory parameter for potentially non-linear and non-Gaussian time series with wavelets. Inverse Problems, 2022, 38, 035004.
[27] Xu C., Zhang Y.*. Estimating adsorption isotherm parameters in chromatography via a virtual injection promoting double feed-forward neural network. Journal of Inverse and Ill-Posed Problems, 2022, 30(5), 693-712.
[26] Dong G. Hintermuller M, Zhang Y. A class of second-order geometric quasilinear hyperbolic PDEs and their application in imaging. SIAM Journal on Imaging Sciences, 2021, 14, 645-688.
[25] Zhang Y, Hofmann B. Two new non-negativity preserving iterative regularization methods for ill-posed inverse problems. Inverse Problems and Imaging, 2021, 15, 229-256.
[24] Zhang Y, Gong R. Second order asymptotical regularization methods for inverse problems in partial differential equations. Journal of Computational and Applied Mathematics, 2020, 375.
[23] Gong R, Hofmann B, Zhang Y*. A new class of accelerated regularization methods, with application to bioluminescence tomography. Inverse Problems, 2020, 36, 055013.
[22] Baravdish G, Svensson O, Gulliksson M, Zhang Y*. Damped second order flow applied to image denoising. IMA Journal of Applied Mathematics, 2019, 84, 1082–1111.
[21] Zhang Y, Yao Z, Forssen P, Fornstedt T. Estimating the rate constant from biosensor data via an adaptive variational Bayesian approach. Annals of Applied Statistics, 2019, 13, 2011-2042.
[20] Zhang Y, Hofmann B, On fractional asymptotical regularization of linear ill-posed problems in Hilbert spaces. Fractional Calculus and Applied Analysis, 2019, 22, 699-721.
[19] Zhang Y*, Hofmann B, On the second-order asymptotical regularization of linear ill-posed inverse problems. Applicable Analysis, 2020, 99, 1000–1025. (该杂志历史最受欢迎文章之一、排名第一；高被引论文) https://www.tandfonline.com/doi/full/10.1080/00036811.2018.1517412
[18] Zhang Y*, Gong R, Gulliksson M, Cheng X. A coupled complex boundary expanding compacts method for inverse source problems. Journal of Inverse and Ill-Posed Problems, 2018, 27, 67-86.
[17] Zhang Y*, Gong R, Cheng X, Gulliksson M. A dynamical regularization algorithm for solving inverse source problems of elliptic partial differential equations. Inverse Problems, 2018, 34, 065001.
[16] Lin G, Cheng X, Zhang Y*. A parametric level set based collage method for an inverse problem in elliptic partial differential equations. Journal of Computational and Applied Mathematics, 2018, 340, 101-121.
[15] Zhang Y*, Forssen P, Fornstedt T, Gulliksson M, Dai X. An adaptive regularization algorithm for recovering the rate constant distribution from biosensor data. Inverse Problems in Science and Engineering, 2018, 26, 1464-1489.
[14] Dai X, Zhang C, Zhang Y, Gulliksson M. Topology optimization of steady Navier-Stokes flow via a piecewise constant level set method. Structural and Multidisciplinary Optimization. 2018, 57, 2193-2203.
[13] Yao Z, Zhang Y, Bai Z, Eddy W. Estimating the number of sources in magnetoencephalography using spiked population eigenvalues. Journal of the American Statistical Association, 2018, 113, 505-518.
[12] Cheng X, Lin G, Zhang Y, Gong R, Gulliksson M. A modified coupled complex boundary method for an inverse chromatography problem. Journal of Inverse and Ill-Posed Problems, 2018, 26, 33-49.
[11] Lin G, Zhang Y*, Cheng X, Gulliksson M, Forssen P, Fornstedt T. A regularizing Kohn-Vogelius formulation for the model-free adsorption isotherm estimation problem in chromatography. Applicable Analysis, 2018, 97, 13-40.
[10] Zhang Y*, Lin G, Gulliksson M, Forssen P, Fornstedt T, Cheng X. An adjoint method in inverse problems of chromatography. Inverse Problems in Science and Engineering, 2017, 25(8), 1112-1137.
[9] Zhang Y*, Lin G, Forssen P, Gulliksson M, Fornstedt T, Cheng X. A regularization method for the reconstruction of adsorption isotherms in liquid chromatography. Inverse Problems, 2016, 32(10), 105005.
[8] Gulliksson M, Holmbom A, Persson J, Zhang Y*. A separating oscillation method of recovering the G-limit in standard and non-standard homogenization problems. Inverse Problems, 2016, 32(2), 025005.
[7] Zhang Y*, Gulliksson M, Hernandez Bennetts V, Schaffernicht E. Reconstructing gas distribution maps via an adaptive sparse regularization algorithm. Inverse Problems in Science and Engineering, 2016, 24(7), 1186-1204.
[6] Zhang Y*, Lukyanenko D, Yagola A. Using Lagrange principle for solving two-dimensional integral equation with a positive kernel. Inverse Problems in Science and Engineering. 2016, 24(5), 811-831.
[5] Zhang Y*, Lukyanenko D, Yagola A. An optimal regularization method for convolution equations on the sourcewise represented set. Journal of Inverse and Ill-Posed Problems. 2016, 23(5), 465-475.
[4] Chen T, Gatchell M, Stockett M, Alexander J, Zhang Y, et al. Absolute fragmentation cross sections in atom-molecule collisions: scaling laws for non-statistical fragmentation of polycyclic aromatic hydrocarbon molecules. The Journal of Chemical Physics. 2014, 140(22) , 224-306.
[3] Zhang Y, Lukyanenko D, Yagola A. Using Lagrange principle for solving linear ill-posed problems with a priori information. Numerical Methods and Programming. 2013, 14, 468-482. (in Russian)
[2] Wang Y, Zhang Y*, Lukyanenko D, Yagola A. Recovering aerosol particle size distribution function on the set of bounded piecewise-convex functions. Inverse Problems in Science and Engineering, 2013, 21, 339-354.
[1] Wang Y, Zhang Y*, Lukyanenko D, Yagola A. A method of restoring the restoring aerosol particle size distribution function on the set of piecewise-convex functions. Numerical Methods and Programming, 2012, 13, 49-66. (in Russian)
