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Found 7 papers, 3 papers with code
Physics-informed Discovery of State Variables in Second-Order and Hamiltonian Systems
The modeling of dynamical systems is a pervasive concern for not only describing but also predicting and controlling natural phenomena and engineered systems.
A Personalised Learning Tool for Physics Undergraduate Students Built On a Large Language Model for Symbolic Regression
Our tool can correctly identify relationships between physics variables for most equations, underscoring its value as a complementary personalized learning tool for undergraduate physics students.
Celestial Machine Learning: Discovering the Planarity, Heliocentricity, and Orbital Equation of Mars with AI Feynman
Can a machine or algorithm discover or learn the elliptical orbit of Mars from astronomical sightings alone?
A Comparative Evaluation of Additive Separability Tests for Physics-Informed Machine Learning
We present and comparatively and empirically evaluate the eight methods to compute the mixed partial derivative of a surrogate function.
Celestial Machine Learning: From Data to Mars and Beyond with AI Feynman
Can a machine or algorithm discover or learn Kepler's first law from astronomical sightings alone?
What's Next? Predicting Hamiltonian Dynamics from Discrete Observations of a Vector Field
We present several methods for predicting the dynamics of Hamiltonian systems from discrete observations of their vector field.
Separable Hamiltonian Neural Networks
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.
