2 papers
hep-ph2026
SMEFT-Pheno-Agent: a natural-language-driven AI agent for machine-learning-assisted Standard Model Effective Field Theory phenomenology
Yu-Chen Guo, Jie Wang, Ji-Chong Yang
We present SMEFT-Pheno-Agent, a Python workflow guided by a natural-language AI agent to perform machine-learning-assisted Standard Model Effective Field Theory (SMEFT) phenomenolo…
hep-ph2025
Enhancing Phase Transition Calculations with Fitting and Neural Network
Ligong Bian, Hongxin Wang, Yang Xiao +3
The computation of bounce action in a phase transition involves solving partial differential equations, inherently introducing non-negligible numerical uncertainty. Deriving charac…