Search Results for author:
Found 6 papers, 1 papers with code
Comment on "Machine learning conservation laws from differential equations"
In lieu of abstract, first paragraph reads: Six months after the author derived a constant of motion for a 1D damped harmonic oscillator [1], a similar result appeared by Liu, Madhavan, and Tegmark [2, 3], without citing the author.
Constants of Motion for Conserved and Non-conserved Dynamics
This paper begins with a dynamical model that was obtained by applying a machine learning technique (FJet) to time-series data; this dynamical model is then analyzed with Lie symmetry techniques to obtain constants of motion.
Extracting Dynamical Models from Data
In this paper, the approach of using machine learning to model the updates of the phase space variables is introduced; this is done as a function of the phase space variables.
2nd-order Updates with 1st-order Complexity
It has long been a goal to efficiently compute and use second order information on a function ($f$) to assist in numerical approximations.
Neograd: Near-Ideal Gradient Descent
The purpose of this paper is to improve upon existing variants of gradient descent by solving two problems: (1) removing (or reducing) the plateau that occurs while minimizing the cost function, (2) continually adjusting the learning rate to an "ideal" value.
Speedup from a different parametrization within the Neural Network algorithm
A different parametrization of the hyperplanes is used in the neural network algorithm.
