Zi-Yu Khoo

20 papers C 2Journal 11Unranked 7
YearRankTypeTitle / Venue / Authors
2025 J jnl
Periodic Hamiltonian Neural Networks.
IEEE Trans. Artif. Intell.
Zi-Yu Khoo, Dawen Wu, Jonathan Sze Choong Low, Stéphane Bressan
2025 — conf
Physics-Informed Discovery of State Variables in Second-Order and Hamiltonian Systems.
ACIIDS (1)
Félix Chavelli, Zi-Yu Khoo, Dawen Wu, Jonathan Sze Choong Low, Stéphane Bressan
2025 J jnl
Prompts to Proxies: Emulating Human Preferences via a Compact LLM Ensemble.
CoRR
Bingchen Wang, Zi-Yu Khoo, Bryan Kian Hsiang Low
2025 J jnl
Rediscovering the Lunar Equation of the Centre with AI Feynman via Embedded Physical Biases.
CoRR
Saumya Shah, Zi-Yu Khoo, Abel Yang, Stéphane Bressan
2025 — conf
Uncovering Scaling Laws for Large Language Models via Inverse Problems.
EMNLP (Findings)
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
2025 J jnl
Uncovering Scaling Laws for Large Language Models via Inverse Problems.
CoRR
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
2024 — conf
A Personalised Learning Tool for Physics Undergraduate Students Built On a Large Language Model for Symbolic Regression.
CAI
Yufan Zhu, Zi-Yu Khoo, Jonathan Sze Choong Low, Stéphane Bressan
2024 J jnl
A Personalised Learning Tool for Physics Undergraduate Students Built On a Large Language Model for Symbolic Regression.
CoRR
Yufan Zhu, Zi-Yu Khoo, Jonathan Sze Choong Low, Stéphane Bressan
2024 J jnl
Physics-informed Discovery of State Variables in Second-Order and Hamiltonian Systems.
CoRR
Félix Chavelli, Zi-Yu Khoo, Dawen Wu, Jonathan Sze Choong Low, Stéphane Bressan
2023 C conf
A Comparative Evaluation of Additive Separability Tests for Physics-Informed Machine Learning.
iiWAS
Zi-Yu Khoo, Jonathan Sze Choong Low, Stéphane Bressan
2023 J jnl
A Comparative Evaluation of Additive Separability Tests for Physics-Informed Machine Learning.
CoRR
Zi-Yu Khoo, Jonathan Sze Choong Low, Stéphane Bressan
2023 — conf
Assessing the Effectiveness of Intrinsic Dimension Estimators for Uncovering the Phase Space Dimensionality of Dynamical Systems from State Observations - A Comparative Analysis.
DEXA (1)
Félix Chavelli, Zi-Yu Khoo, Jonathan Sze Choong Low, Stéphane Bressan
2023 C conf
Celestial Machine Learning - Discovering the Planarity, Heliocentricity, and Orbital Equation of Mars with AI Feynman.
iiWAS
Zi-Yu Khoo, Gokul Rajiv, Abel Yang, Jonathan Sze Choong Low, Stéphane Bressan
2023 — conf
Celestial Machine Learning - From Data to Mars and Beyond with AI Feynman.
DEXA (2)
Zi-Yu Khoo, Abel Yang, Jonathan Sze Choong Low, Stéphane Bressan
2023 J jnl
Celestial Machine Learning: Discovering the Planarity, Heliocentricity, and Orbital Equation of Mars with AI Feynman.
CoRR
Zi-Yu Khoo, Gokul Rajiv, Abel Yang, Jonathan Sze Choong Low, Stéphane Bressan
2023 J jnl
Celestial Machine Learning: From Data to Mars and Beyond with AI Feynman.
CoRR
Zi-Yu Khoo, Abel Yang, Jonathan Sze Choong Low, Stéphane Bressan
2023 J jnl
Separable Hamiltonian Neural Networks.
CoRR
Zi-Yu Khoo, Jonathan Sze Choong Low, Stéphane Bressan
2023 J jnl
What's Next? Predicting Hamiltonian Dynamics from Discrete Observations of a Vector Field.
CoRR
Zi-Yu Khoo, Delong Zhang, Stéphane Bressan
2022 — conf
What's Next? Predicting Hamiltonian Dynamics from Discrete Observations of a Vector Field.
DEXA (2)
Zi-Yu Khoo, Delong Zhang, Stéphane Bressan
2021 — conf
Neural Ordinary Differential Equations for the Regression of Macroeconomics Data Under the Green Solow Model.
DEXA (1)
Zi-Yu Khoo, Kang Hao Lee, Zhibo Huang, Stéphane Bressan