GWKokab is a new modular JAX framework that uses normalizing flow samplers for efficient inference on subpopulations of compact binary mergers.
Rosenblatt, Psychological review65, 386 (1958)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
The paper introduces neural-network trial wave functions for variational Monte Carlo, frames the variational method as unsupervised learning, and illustrates the approach on the Yukawa potential and hydrogen molecule.
citing papers explorer
-
An Implementation to Identify the Properties of Multiple Population of Gravitational Wave Sources
GWKokab is a new modular JAX framework that uses normalizing flow samplers for efficient inference on subpopulations of compact binary mergers.
-
Introduction to the artificial neural network-based variational Monte Carlo method
The paper introduces neural-network trial wave functions for variational Monte Carlo, frames the variational method as unsupervised learning, and illustrates the approach on the Yukawa potential and hydrogen molecule.