2 citations · 6 across the 9 of their papers we have counts for
10 papers
Rediscovering the Lunar Equation of the Centre with AI Feynman via Embedded Physical Biases
Saumya Shah, Zi-Yu Khoo, Abel Yang +1
This work explores using the physics-inspired AI Feynman symbolic regression algorithm to automatically rediscover a fundamental equation in astronomy -- the Equation of the Centre…
Uncovering Scaling Laws for Large Language Models via Inverse Problems
Arun Verma, Zhaoxuan Wu, Zijian Zhou +15
Large Language Models (LLMs) are large-scale pretrained models that have achieved remarkable success across diverse domains. These successes have been driven by unprecedented compl…
Prompts to Proxies: Emulating Human Preferences via a Compact LLM Ensemble
Bingchen Wang, Zi-Yu Khoo, Jingtan Wang
Large language models are increasingly used as proxies for human subjects in social science research, yet external validity requires that synthetic agents faithfully reflect the pr…
Physics-informed Discovery of State Variables in Second-Order and Hamiltonian Systems
Félix Chavelli, Zi-Yu Khoo, Dawen Wu +2
The modeling of dynamical systems is a pervasive concern for not only describing but also predicting and controlling natural phenomena and engineered systems. Current data-driven a…
A Personalised Learning Tool for Physics Undergraduate Students Built On a Large Language Model for Symbolic Regression
Yufan Zhu, Zi-Yu Khoo, Jonathan Sze Choong Low +1
Interleaved practice enhances the memory and problem-solving ability of students in undergraduate courses. We introduce a personalized learning tool built on a Large Language Model…
Celestial Machine Learning: Discovering the Planarity, Heliocentricity, and Orbital Equation of Mars with AI Feynman
Zi-Yu Khoo, Gokul Rajiv, Abel Yang +2
Can a machine or algorithm discover or learn the elliptical orbit of Mars from astronomical sightings alone? Johannes Kepler required two paradigm shifts to discover his First Law…
A Comparative Evaluation of Additive Separability Tests for Physics-Informed Machine Learning
Zi-Yu Khoo, Jonathan Sze Choong Low, Stéphane Bressan
Many functions characterising physical systems are additively separable. This is the case, for instance, of mechanical Hamiltonian functions in physics, population growth equations…
Celestial Machine Learning: From Data to Mars and Beyond with AI Feynman
Zi-Yu Khoo, Abel Yang, Jonathan Sze Choong Low +1
Can a machine or algorithm discover or learn Kepler's first law from astronomical sightings alone? We emulate Johannes Kepler's discovery of the equation of the orbit of Mars with…
What's Next? Predicting Hamiltonian Dynamics from Discrete Observations of a Vector Field
Zi-Yu Khoo, Delong Zhang, Stéphane Bressan
We present several methods for predicting the dynamics of Hamiltonian systems from discrete observations of their vector field. Each method is either informed or uninformed of the…
Separable Hamiltonian Neural Networks
Zi-Yu Khoo, Dawen Wu, Jonathan Sze Choong Low +1
Hamiltonian neural networks (HNNs) are state-of-the-art models that regress the vector field of a dynamical system under the learning bias of Hamilton's equations. A recent observa…