Hypothesis-driven construction of mesoscopic dynamics
Li, Z., Zhu, A., & Li. Q. (2026). Hypothesis-driven construction of mesoscopic dynamics. arXiv preprint arXiv:2605.16211.
Publications
In-context modeling as a retrain-free paradigm for foundation models in computational science
Li, L., Li, Z., Li, S., Zhan, K., Gao, H., Chen, C., & Yang, L. (2026). In-context modeling as a retrain-free paradigm for foundation models in computational science. arXiv preprint arXiv:2604.23098.
Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data Bootstrapping
Yang, P., Feng, Y., Chen, Z., Wu, Y., & Li, Z. (2025). Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data Bootstrapping. in Advances in Neural Information Processing Systems, 38.
Fluorescent protein-based ticker tapes for multiplexed recordings of transcriptional histories in single cells in culture and in vivo
Wang, R., Jiang, J., Li, Z., Liu, T., Wang, Y., Xie, M., & Piatkevich, K. D. (2025). Fluorescent protein-based ticker tapes for multiplexed recordings of transcriptional histories in single cells in culture and in vivo. bioRxiv 2025.09.08.675004.
State-observation augmented diffusion model for nonlinear assimilation with unknown dynamics
Li, Z., Dong, B., & Zhang, P. (2025). State-observation augmented diffusion model for nonlinear assimilation with unknown dynamics. Journal of Computational Physics, page 114240.
Latent assimilation with implicit neural representations for unknown dynamics
Li, Z., Dong, B., & Zhang, P. (2024). Latent assimilation with implicit neural representations for unknown dynamics. Journal of Computational Physics, page 112953.
Learning to simulate partially known spatio-temporal dynamics with trainable difference operators
Huang, X., Li, Z., Liu, H., Wang, Z., Zhou, H., Dong, B., & Hua, B. (2023). Learning to simulate partially known spatio-temporal dynamics with trainable difference operators. arXiv preprint arXiv:2307.14395.