3 papers
hep-ph2025
Uncovering Singularities in Feynman Integrals via Machine Learning
Yuanche Liu, Yingxuan Xu, Yang Zhang
We introduce a machine-learning framework based on symbolic regression to extract the full symbol alphabet of multi-loop Feynman integrals. By targeting the analytic structure rath…
hep-ph2025
Symbolic Reduction of Multi-loop Feynman Integrals via Generating Functions
Bo Feng, Xiang Li, Yuanche Liu +2
We introduce a novel, systematic method for the complete symbolic reduction of multi-loop Feynman integrals, leveraging the power of generating functions. The differential equation…
physics.comp-ph2025
Renormalization-Inspired Effective Field Neural Networks for Scalable Modeling of Classical and Quantum Many-Body Systems
Xi Liu, Yujun Zhao, Chun Yu Wan +2
We introduce Effective Field Neural Networks (EFNNs), a new architecture based on continued functions -- mathematical tools used in renormalization to handle divergent perturbative…