47 citations · 66 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
BelMan: Bayesian Bandits on the Belief--Reward Manifold
Debabrota Basu, Pierre Senellart, Stéphane Bressan
We propose a generic, Bayesian, information geometric approach to the exploration--exploitation trade-off in multi-armed bandit problems. Our approach, BelMan, uniformly supports p…