Ming Zhong

Ming Zhong

Assistant Professor

Publications

In Preparation

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

[J] B. Hao, C. Liu, U. Braga-Neto, M. Zhong, Structure Preserving PINNs for Solving Time Dependent PDEs with Periodic Boundary.

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

Under review

[C] R. Wang, M. Zhong, K. Xu, LG Sanchez-Cortes, IC Guerra, PINNs-Based Uncertainty Quantification for Transient Stability Analysis, submitted, 2023.

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

[J] M. Zhong, D. Liu, A. Arroyave, and U. Braga-Neto. Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes, 2023.

[J] 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.

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

Journal

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

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

  3. 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.

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

  5. 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.

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

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

  8. 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.

  9. 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.

Conference Publications

  1. Z. Guo, I. Cialenco, M. Zhong, Learning Stochastic Dynamics from Data, accepted to ICLR, 2024.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

ArXiv PrePrint

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

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

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

Thesis

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