13 citations · 24 across the 3 of their papers we have counts for
3 papers
astro-ph.IM2022★ 6 cited
Automated discovery of interpretable gravitational-wave population models
Kaze W. K Wong, Miles Cranmer
We present an automatic approach to discover analytic population models for gravitational-wave (GW) events from data. As more gravitational-wave (GW) events are detected, flexible…
astro-ph.GA2022★ 13 cited
GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
Aritra Ghosh, C. Megan Urry, Amrit Rau +11
We introduce a novel machine learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large numbers of galaxies. The Galaxy Morphology…
physics.flu-dyn2022★ 5 cited
TNT: Vision Transformer for Turbulence Simulations
Yuchen Dang, Zheyuan Hu, Miles Cranmer +2
Turbulence is notoriously difficult to model due to its multi-scale nature and sensitivity to small perturbations. Classical solvers of turbulence simulation generally operate on f…