Scientific machine learning

I have been increasingly interested in ML/AI, particularly in integrating mathematical and physical structures into neural networks. Therefore, I mainly focus on structure-preserving ML. Scientific machine learning (SciML) offers a promising approach to tackling problems where traditional numerical algorithms fall short. For instance, when designed properly, SciML can provide an alternative method for studying dynamical systems.

The following structure-preserving SciML work has been primarily supported by DOE ASCR data-intensive scientific machine learning program.

Structure-preserving SciML work

The recent representative papers include: dynamics learning for multiscale and/or stiff ODEs1 2 3 and large-scale PDEs,4 learning flow maps for Hamiltonians 5 6, their applications,7 8 and molecular dynamics.9 10 Please refer to the corresponding references for more details.

The following fusion ML work has been primarily supported by DOE FES SciDAC and FES AI/ML programs.

Fast ML-based surrogates for fusion modeling

We have developed novel symplectic neural network (HenonNet) and applied it as a fast surrogate to generate Poincaré plot for tokamaks.11 We also developed physics-assisted latent dynamics learning for collisional radiative models.12 13 Please refer to the corresponding references for more details.

  1. D. Serino, A. Alvarez Loya, J. W. Burby, I. G. Kevrekidis, and Q. Tang. Fast-slow neural networks for learning singularly perturbed dynamical systems, Journal of Computational Physics, 537:114090, 2025. ↩

  2. A. Alvarez Loya, D. Serino, J. Burby, and Q. Tang. Structure-preserving neural ordinary differential equations for stiff systems, submitted, 16 pages, 2025. ↩

  3. Y. Lu, X. Li, C. Liu, Q. Tang, and Y. Wang. Learning generalized diffusions using an energetic variational approach, submitted, 23 pages, CiCP, 2024. ↩

  4. 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. ↩

  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. Y. Chen, W. Guo, Q. Tang, X. Zhong. Reduced-order modeling of Hamiltonian dynamics based on symplectic neural networks, submitted, 25 pages, 2025. ↩

  7. E. G. Drimalas, F. Fraschetti, C.-K. Huang, and Q. Tang. Symplectic neural network and its applications to charged particle dynamics in electromagnetic fields, in revision, PoP, 16 pages, 2025. ↩

  8. C.-K. Huang, Q. Tang, et al. Symplectic neural surrogate models for beam dynamics. Journal of Physics: Conference Series, 2687(6):062026, 2024. ↩

  9. H. Tischler, W. Li, Q. Tang, D. Perez, and T. Vogel. Predicting atomistic transitions with transformers, submitted, 10 pages, 2025. ↩

  10. S.-H. Wang, Y. Huang, J. M. Baker, Y.-E. Sun, Q. Tang, and B. Wang. A theoretically-principled sparse, connected, and rigid graph representation of molecules, International Conference on Learning Representations (ICLR), 2025. ↩

  11. J. W. Burby, Q. Tang and R. Maulik. Fast neural Poincare maps for toroidal magnetic fields, Plasma Physics and Controlled Fusion, 63:024001, 2020. ↩

  12. 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. ↩

  13. X. Xie, Q. Tang, and X.-Z. Tang. Latent space dynamics learning for stiff collisional-radiative models, Machine Learning: Science and Technology, 5:045070, 2024. ↩