5 papers
CHESS: CHEbyshev pSeudo-Spectral transport for Feynman integral differential equations
Yuanche Liu, Yang Zhang
We present CHESS (CHEbyshev pSeudo Spectrum), a Wolfram Language package for high-precision one-dimensional transport of ε-factorized differential equations for Feynman master int…
Modeling Quantum Geometry for Fractional Chern Insulators with unsupervised learning
Ang-Kun Wu, Louis Primeau, Jingtao Zhang +3
Fractional Chern insulators (FCIs) in moire materials present a unique platform for exploring strongly correlated topological phases beyond the paradigm of ideal quantum geometry.…
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…
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…
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…