1.4k citations · 3.9k across the 30 of their papers we have counts for
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AI Poincaré: Machine Learning Conservation Laws from Trajectories
Ziming Liu, Max Tegmark
We present AI Poincaré, a machine learning algorithm for auto-discovering conserved quantities using trajectory data from unknown dynamical systems. We test it on five Hamiltonian…
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng +3
We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal, in the sense of having the best accuracy for a given complexity. I…
Foreground modelling via Gaussian process regression: an application to HERA data
Abhik Ghosh, Florent Mertens, Gianni Bernardi +70
The key challenge in the observation of the redshifted 21-cm signal from cosmic reionization is its separation from the much brighter foreground emission. Such separation relies on…
Redundant-Baseline Calibration of the Hydrogen Epoch of Reionization Array
Joshua S. Dillon, Max Lee, Zaki S. Ali +76
In 21 cm cosmology, precision calibration is key to the separation of the neutral hydrogen signal from very bright but spectrally-smooth astrophysical foregrounds. The Hydrogen Epo…