Adaptable Hamiltonian neural networks. - Chen-Di Han; Bryan Glaz; Mulugeta A. Haile; Ying-Cheng Lai (CoRR, 2021)

Tomography of time-dependent quantum spin networks with machine learning. - Chen-Di Han; Bryan Glaz; Mulugeta A. Haile; Ying-Cheng Lai (CoRR, 2021)

Digital twins of nonlinear dynamical systems. - Ling-Wei Kong; Yang Weng; Bryan Glaz; Mulugeta A. Haile; Ying-Cheng Lai (CoRR, 2022)

Model-free tracking control of complex dynamical trajectories with machine learning. - Zheng-Meng Zhai; Mohammadamin Moradi; Ling-Wei Kong; Bryan Glaz; Mulugeta A. Haile; Ying-Cheng Lai (CoRR, 2023)

Machine-learning parameter tracking with partial state observation. - Zheng-Meng Zhai; Mohammadamin Moradi; Bryan Glaz; Mulugeta A. Haile; Ying-Cheng Lai (CoRR, 2023)

Machine-learning prediction of tipping and collapse of the Atlantic Meridional Overturning Circulation. - Shirin Panahi; Ling-Wei Kong; Mohammadamin Moradi; Zheng-Meng Zhai; Bryan Glaz; Mulugeta A. Haile; Ying-Cheng Lai (CoRR, 2024)

Pseudometrics for scalable data-driven comparisons of nonlinear dynamical systems. - Bryan Glaz (CoRR, 2024)

Learning to learn ecosystems from limited data - a meta-learning approach. - Zheng-Meng Zhai; Bryan Glaz; Mulugeta A. Haile; Ying-Cheng Lai (CoRR, 2024)

Unsupervised learning for anticipating critical transitions. - Shirin Panahi; Ling-Wei Kong; Bryan Glaz; Mulugeta A. Haile; Ying-Cheng Lai (CoRR, 2025)