Complete parameter inference for GW150914 using deep learning

7 August 2020

Papers citing "Complete parameter inference for GW150914 using deep learning"

9 / 9 papers shown

Title
Comparative Study of Coupling and Autoregressive Flows through Robust Statistical Tests A. Coccaro Marco Letizia H. Reyes-González Riccardo Torre OOD 48 5 0 23 Feb 2023
Adapting to noise distribution shifts in flow-based gravitational-wave inference J. Wildberger Maximilian Dax Stephen R. Green J. Gair M. Purrer Jakob H. Macke A. Buonanno Bernhard Schölkopf AI4CE 34 9 0 16 Nov 2022
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference Patrick W Cannon Daniel Ward Sebastian M. Schmon 35 36 0 05 Sep 2022
Inference-optimized AI and high performance computing for gravitational wave detection at scale Pranshu Chaturvedi Asad Khan Minyang Tian Eliu A. Huerta Huihuo Zheng 25 29 0 26 Jan 2022
Group equivariant neural posterior estimation Maximilian Dax Stephen R. Green J. Gair Michael Deistler Bernhard Schölkopf Jakob H. Macke BDL 36 31 0 25 Nov 2021
Real-time gravitational-wave science with neural posterior estimation Maximilian Dax Stephen R. Green J. Gair Jakob H. Macke A. Buonanno Bernhard Schölkopf 34 133 0 23 Jun 2021
A deep generative model for probabilistic energy forecasting in power systems: normalizing flows Jonathan Dumas Antoine Wehenkel Bertrand Cornélusse Antonio Sutera AI4TS 32 82 0 17 Jun 2021
Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources Eliu A. Huerta Zhizhen Zhao 42 22 0 13 May 2021
Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning M. Edwards 21 13 0 15 Sep 2020