Defer to other experts, but (briefly) normalizing flows are a method for constructing complex distributions by transforming a probability density through a series of invertible transformations. Normalizing flows are trained using
a plain log-likelihood function, and they are capable of exact density evaluation and efficient sampling. See:
Laurent Dinh, David Krueger, and Yoshua Bengio. Nice: Non-linear independent components estimation. In ICLR Workshop, 2015. Link: https://arxiv.org/pdf/1410.8516
And for your direct question, the following paper "Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods" appears upon a superficial glance to be relevant. Link: https://arxiv.org/pdf/2107.08001
Danilo Rezende and Shakir Mohamed. Variational inference with normalizing flows. In ICML, 2015. Link: https://bigdata.duke.edu/wp-content/uploads/2022/08/1505.057...
Laurent Dinh, David Krueger, and Yoshua Bengio. Nice: Non-linear independent components estimation. In ICLR Workshop, 2015. Link: https://arxiv.org/pdf/1410.8516
And for your direct question, the following paper "Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods" appears upon a superficial glance to be relevant. Link: https://arxiv.org/pdf/2107.08001