55 citations · 125 across the 20 of their papers we have counts for
8 papers · 1 filter
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…
Extending JumpProcess.jl for fast point process simulation with time-varying intensities
Guilherme Augusto Zagatti, Samuel A. Isaacson, Christopher Rackauckas +3
Point processes model the occurrence of a countable number of random points over some support. They can model diverse phenomena, such as chemical reactions, stock market transactio…
Confidential Truth Finding with Multi-Party Computation (Extended Version)
Angelo Saadeh, Pierre Senellart, Stéphane Bressan
Federated knowledge discovery and data mining are challenged to assess the trustworthiness of data originating from autonomous sources while protecting confidentiality and privacy.…
Supervised Training of Neural-Network Quantum States for the Next Nearest Neighbor Ising model
Zheyu Wu, Remmy Zen, Heitor P. Casagrande +2
Different neural network architectures can be unsupervisedly or supervisedly trained to represent quantum states. We explore and compare different strategies for the supervised tra…