Publications

In Preparation

  1. Z. Guo, I. Cialenco, M. Zhong, Noise Guided Learning Stochastic Dynamics from High Dimensional Data.

  2. B. Hao, U. Braga-Neto, C. Liu, L. Wang, M. Zhong, Structure Preserving PINNs for Solving Time Dependent PDEs with Periodic Boundary.

  3. X. Lu, M. Zhong, E. Oran, and U. Braga-Neto. Physics Informed Artificial Viscosity for Systems of Conservation Laws.

Under review

  1. M. Zhong, J. Miller, and M. Maggioni. Machine learning for discovering effective interaction kernels between celestial bodies from ephemerides, 2022.

  2. X. Chen, D. J. Jeffery, M. Zhong, L. McClenny, U. Braga-Neto, and L. Wang. Using physics informed neural networks for supernova radiative transfer simulation, 2022.

  3. Y. Shang, J. Feng, Y. Lu, Y. Yan and M. Zhong. Training-Stabilized Diffusion Models via Adding and Reducing Noise, submitted, 2023.

  4. R. Wang, M. Zhong, K. Xu, LG Sanchez-Cortes, IC Guerra, PINNs-Based Uncertainty Quantification for Transient Stability Analysis, submitted, 2024.

  5. M. Zhong, D. Liu, A. Arroyave, and U. Braga-Neto. Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes, 2024.

Published

  1. B. Ganis, I. Yotov, and M. Zhong. A stochastic mortar mixed finite element method for flow in porous media with multiple rock types. SIAM J. Sci. Comp., 33(3):1439 – 1474, 2011.

  2. F. Lu, M. Zhong, S. Tang, and M. Maggioni. Nonparametric inference of interaction laws in systems of agents from trajectory data. Proceedings of the National Academy of Sciences, 116(29): 14424 – 14433, 2019.

  3. M. Zhong, J. Miller, and M. Maggioni. Data-driven discovery of emergent behaviors in collective dynamics. Physica D: nonlinear phenomenon, 411:132542, 2020.

  4. M. Maggioni, J. Miller, H. Qiu, and M. Zhong. Learning interaction kernels for agent systems on riemannian manifolds. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 7290 – 7300, PMLR, 2021.

  5. S. Foucart, E. Tadmor, and M. Zhong. On the sparsity of lasso minimizers in sparse data recovery. Constructive Approximation, 2022.

  6. J. Feng, M. Maggioni, P. Martin, and M. Zhong. Learning interaction variables and kernels from observations of agent-based systems. IFAC-PapersOnLine, 55(30):162 – 167, 2022. 25th International Symposium on Mathematical Theory of Networks and Systems, 2022.

  7. J. Park, C. Saltijeral, and M. Zhong. Grassmanian packings: Trust region stochastic tuning for matrix incoherence. 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 1 - 6, 2022.

  8. E. J. R. Coutinho, M. Dall’Aqua, L. McClenny, M. Zhong, U. Braga-Neto, and E. Gildin. Stabilized hyperbolic pde solver by adding adaptive localized artificial viscosity to physics-informed neural networks, Journal of Computational Physics, 2023.

  9. J. Greene, E. Tadmor, and M. Zhong. The emergence of social hierarchy in collective motion of living matters, Physical Biology, 2023.

  10. B. Duan, Y. Yan, and M. Zhong. Towards saner deep image registration, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 12459-12468.

  11. J. Miller, S. Tang, M. Zhong, and M. Maggioni. Learning theory for inferring interaction kernels in second-order interacting agent systems, Sampling Theory, Signal Processing, and Data Analysis, 2023.

  12. B. Hao, M. Zhong, K. O’Keeffe, Attractive and repulsive interactions in the one-dimensional swarmalator model, Physical Review E, 108(6), 064214, 2023.

  13. J. Feng, and M. Zhong. Learning Collective Behaviors from Obsrvation, 2024.

  14. Z. Guo, I. Cialenco, M. Zhong, Learning Stochastic Dynamics from Data, ICLR, 2024.

ArXiv PrePrint

  1. N. Mays and M. Zhong. Iterative Refinement of A Modified Lavrentiev Regularization Method for De-convolution of the Discrete Helmholtz Type Differential Filter, 2018.

  2. M. Zhong. Time Relaxation with Iterative Modified Lavrentiev Regularization, 2018.

  3. T. Gerew and M. Zhong. Swarmalators that flock and sync, 2023.

Thesis

  1. M. Zhong. Hierarchical Reconstruction Method for Solving Ill-posed Linear Inverse Problems, PhD thesis, University of Maryland, College Park, MD, 2016.