Publications

Preprints (available on Google Scholar)

  1. D. Serino, Q. Tang, X.-Z. Tang, T. V. Kolev, and K. Lipnikov. An adaptive Newton-based free-boundary Grad–Shafranov solver, submitted, 23 pages, 2024.
  2. X. Xie, Q. Tang, and X.-Z. Tang. Latent space dynamics learning for stiff collisional-radiative models, submitted, 27 pages, 2024.
  3. D. Serino, A. Alvarez Loya, J. W. Burby, I. G. Kevrekidis, and Q. Tang. Intelligent attractors for singularly perturbed dynamical systems, submitted, 32 pages, 2024.
  4. Z. Jorti, Q. Tang, K. Lipnikov, and X.-Z. Tang. A mimetic finite difference based quasi-static magnetohydrodynamic solver for force-free plasmas in tokamak disruptions, submitted, 43 pages, 2023.

Journal Publications

  1. J. Rudi, M. Heldman, E. M. Constantinescu, Q. Tang, and X.-Z. Tang. Scalable implicit solvers with dynamic mesh adaptation for a relativistic drift-kinetic Fokker–Planck–Boltzmann model, Journal of Computational Physics, 507:112954, 2024.
  2. M. J. Picklo, Q. Tang, Y. Zhang, J. K. Ryan, and X.-Z. Tang. Denoising Particle-In-Cell Data via Smoothness-Increasing Accuracy-Conserving Filters with Application to Bohm Speed Computation, Journal of Computational Physics, 502:112790, 2024.
  3. H. Ji, L. Li and Q. Tang. Numerical methods for fourth-order PDEs on overlapping grids with application to Kirchhoff-Love plates, Journal of Scientifc Computing, 98(2):41, 2024.
  4. C.-K. Huang, Q. Tang, et al. Symplectic neural surrogate models for beam dynamics. Journal of Physics: Conference Series, 2687(6):062026, 2024.
  5. V. Duruisseaux, J. W. Burby, and Q. Tang. Approximation of Nearly-Periodic Symplectic Maps via Structure-Preserving Neural Networks, Scientific Reports, 13.1:8351, 2023.
  6. A. J. Linot, J. W. Burby, Q. Tang, P. Balaprakash, M. D. Graham, and R. Maulik. Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems, Journal of Computational Physics, 474:111838, 2023.
  7. N. A. Garland, R. Maulik, Q. Tang, X.-Z. Tang and P. Balaprakash. Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling, Machine Learning: Science and Technology, 3:045003, 2022.
  8. Q. Tang, L. Chacon, T. V. Kolev, J. N. Shadid and X.-Z. Tang. An adaptive scalable fully implicit algorithm based on stabilized finite element for reduced visco-resistive MHD, Journal of Computational Physics, 454:110967, 2022
  9. S. Liu, Q. Tang and X.-Z. Tang. A parallel cut-cell algorithm for the free-boundary Grad-Shafranov problem, SIAM Journal on Scientific Computing, 43.6:B1198–B1225, 2021.
  10. J. W. Burby, Q. Tang and R. Maulik. Fast neural Poincare maps for toroidal magnetic fields, Plasma Physics and Controlled Fusion, 63:024001, 2020.
  11. Z. Peng, Q. Tang and X.-Z. Tang. An adaptive discontinuous Petrov-Galerkin method for the Grad-Shafranov equation, SIAM Journal on Scientific Computing, 42.5:B1227–B1249, 2020.
  12. L. Fu and Q. Tang. High-order low-dissipation targeted ENO schemes for ideal magnetohydrodynamics, Journal of Scientific Computing, 80(1):692–716, 2019.
  13. A. J. Christlieb, X. Feng, Y. Jiang and Q. Tang. A high-order finite difference WENO scheme for ideal magnetohydrodynamics on curvilinear meshes, SIAM Journal on Scientific Computing, 40.4:A2631–A2666, 2018.
  14. J. W. Banks, W. D. Henshaw, D. W. Schwendeman and Q. Tang. A stable partitioned FSI algorithm for rigid bodies and incompressible flow in three dimensions, Journal of Computational Physics, 373:455–492, 2018.
  15. Y. Liu, Q. Tang and B. Wu. Space charge effect of the time-varying electron injection in a diode: classical and relativistic regimes, Physics of Plasmas, 24:093512, 2017.
  16. J. W. Banks, W. D. Henshaw, D. W. Schwendeman and Q. Tang. A stable partitioned FSI algorithm for rigid bodies and incompressible flow. Part II: General formulation, Journal of Computational Physics, 343:469–500, 2017.
  17. J. W. Banks, W. D. Henshaw, D. W. Schwendeman and Q. Tang. A stable partitioned FSI algorithm for rigid bodies and incompressible flow. Part I: Model problem analysis, Journal of Computational Physics, 343:432–468, 2017.
  18. A. J. Christlieb, X. Feng, D. C. Seal and Q. Tang. A high-order positivity-preserving single-stage single-step method for the ideal magnetohydrodynamic equations, Journal of Computational Physics, 316:218–242, 2016.
  19. Z. Wang, Q. Tang, W. Guo and Y. Cheng, Sparse grid discontinuous Galerkin methods for high-dimensional elliptic equations, Journal of Computational Physics, 314:244–263, 2016.
  20. D. C. Seal, Q. Tang, A. J. Christlieb, and Z. Xu. An explicit high-order single-stage single-step positivity-preserving finite difference WENO method for the compressible Euler equations, Journal of Scientific Computing, 68(1):171–190, 2016.
  21. A. J. Christlieb, Y. Liu, Q. Tang and Z. Xu. Positivity-preserving finite difference weighted ENO schemes with constrained transport for ideal magnetohydrodynamic equations, SIAM Journal on Scientific Computing, 37.4:A1825–A1845, 2015.
  22. A. J. Christlieb, Y. Liu, Q. Tang and Z. Xu. High order parametrized maximum-principle-preserving and positivity-preserving WENO schemes on unstructured meshes, Journal of Computational Physics, 281:334–351, 2015
  23. A. J. Christlieb, J. A. Rossmanith and Q. Tang. Finite difference weighted essentially non-oscillatory schemes with constrained transport for ideal magnetohydrodynamics, Journal of Computational Physics, 268:302–325, 2014.

Conference Proceedings

  1. C.-K. Huang, ..., Q. Tang, et al. Modeling of nonlinear beam dynamics via a novel particle-mesh method and surrogate models with symplectic neural networks, Proceedings of North American Particle Accelerator Conference, 3 pages, 2022.
  2. N. A. Garland, R. Maulik, Q. Tang, X.-Z. Tang and P. Balaprakash. Progress towards high fidelity collisional-radiative model surrogates for rapid in-situ evaluation, Machine learning for Physical Sciences workshop, NeurIPS, 7 pages, 2020.

Techinical Reports