2 citations · 6 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…