Neural network impurity solver for real-frequency dynamical mean-field theory

arXiv:2511.14505 · cond-mat.str-el · Submitted 2025-11-18 · Read on arXiv

cond-mat.str-el

Submitted: 2025-11-18

Updated: 2025-11-18

Terminology

• Transformer — 7× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Consistency — 6× in this paper · explained in 3 episodes
• Loss Function — 6× in this paper · explained in 2 episodes
• Self-consistency — 6× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Attention Mechanism — 3× in this paper · explained in 3 episodes
• Cross-Attention Mechanism — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Hubbard Model — 3× in this paper · explained in 3 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Fourier Analysis Network — 2× in this paper
• Activation Function — 1× in this paper · explained in 1 episode
• Regularization — 1× in this paper · explained in 3 episodes
• Uniform Manifold Approximation and Projection — 1× in this paper

Sources

• Machine learning for many-body physics: efficient solution of dynamical mean-field theory
• Predicting interacting Green's functions with neural networks
• Physics-informed neural network model for quantum impurity problems based on Lehmann representation
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• Neural Machine Translation by Jointly Learning to Align and Translate
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• FAN: Fourier Analysis Networks
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• Adam: A Method for Stochastic Optimization

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