A physics-informed machine learning approach for analytic continuation of Green’s functions. Uses a Variational Autoencoder (VAE) to reconstruct spectral functions by explicitly learning poles and residues from imaginary-time Quantum Monte Carlo data. Robust, interpretable, and designed for noisy inputs.

A testing toolkit for analytic continuation methods and codes

A web-based graphic user interface for ACFlow