flowMC
flowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAX - Published in JOSS (2023)
InvertibleNetworks.jl
InvertibleNetworks.jl: A Julia package for scalable normalizing flows - Published in JOSS (2024)
pocoMC
pocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmology - Published in JOSS (2022)
LINFA
LINFA: a Python library for variational inference with normalizing flow and annealing - Published in JOSS (2024)
SBIAX
SBIAX: Density-estimation simulation-based inference in JAX - Published in JOSS (2025)
Surjectors
Surjectors: surjection layers for density estimation with normalizing flows - Published in JOSS (2024)
pythae
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
wiser.jl
WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction
fno-nf.jl
Solving multiphysics-based inverse problems with learned surrogates and constraints
ContinuousNormalizingFlows
Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia
https://github.com/acerbilab/normalizing-flow-regression
Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluations
https://github.com/compvis/net2net
Network-to-Network Translation with Conditional Invertible Neural Networks
ebflow
[NeurIPS 2023] Training Energy-Based Normalizing Flow with Score-Matching Objectives