| 2025 | ACIIDS | Physics-Informed Discovery of State Variables in Second-Order and Hamiltonian Systems. | Flix Chavelli, Zi-Yu Khoo, Dawen Wu, Jonathan Sze Choong Low, Stphane Bressan |
| 2025 | EMNLP | Uncovering Scaling Laws for Large Language Models via Inverse Problems. | Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin, Zhiliang Chen, Rachael Hwee Ling Sim, Rui Qiao, Jingtan Wang, Nhung Bui, Xinyuan Niu, Wenyang Hu, Gregory Kang Ruey Lau, Zi-Yu Khoo, Zitong Zhao, Xinyi Xu, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low |
| 2023 | DEXA | Assessing the Effectiveness of Intrinsic Dimension Estimators for Uncovering the Phase Space Dimensionality of Dynamical Systems from State Observations - A Comparative Analysis. | Flix Chavelli, Zi-Yu Khoo, Jonathan Sze Choong Low, Stphane Bressan |
| 2023 | DEXA | Celestial Machine Learning - From Data to Mars and Beyond with AI Feynman. | Zi-Yu Khoo, Abel Yang, Jonathan Sze Choong Low, Stphane Bressan |
| 2023 | IIWAS | A Comparative Evaluation of Additive Separability Tests for Physics-Informed Machine Learning. | Zi-Yu Khoo, Jonathan Sze Choong Low, Stphane Bressan |
| 2023 | IIWAS | Celestial Machine Learning - Discovering the Planarity, Heliocentricity, and Orbital Equation of Mars with AI Feynman. | Zi-Yu Khoo, Gokul Rajiv, Abel Yang, Jonathan Sze Choong Low, Stphane Bressan |
| 2022 | DEXA | What's Next? Predicting Hamiltonian Dynamics from Discrete Observations of a Vector Field. | Zi-Yu Khoo, Delong Zhang, Stphane Bressan |
| 2021 | DEXA | Neural Ordinary Differential Equations for the Regression of Macroeconomics Data Under the Green Solow Model. | Zi-Yu Khoo, Kang Hao Lee, Zhibo Huang, Stphane Bressan |