Physics-informed neural network model for quantum impurity problems based on Lehmann representation

Fumiya Kakizawa, Satoshi Terasaki, Hiroshi Shinaoka

We propose a physics-informed neural network (PINN) model to efficiently predict the self-energy of Anderson impurity models (AIMs) based on the Lehmann representation. As an examp…