Spectral functions in Minkowski quantum electrodynamics from neural reconstruction: Benchmarking against dispersive Dyson–Schwinger integral equations
Rodrigo Carmo Terin
闵可夫斯基物理学的神经网络方法(M-PINN)是直接在闵可夫斯基时空解决量子电动力学(QED)的Dyson-Schwinger积分方程(DSE)的。 我们的新策略合并了两种互补的方法:(i)基于莱曼表示和减去分散关系的分散式求解器,以及(ii)学习费米子质量函数B(p^2)的M-PINN,在相同的截断和重整化配置(淬火,彩虹,Landau量表)下,将DSE残差与多尺度正则化和单调的损耗集成在一起。空间。 基准显示,在壳内和动量减法方案中,从红外(IR)到紫外线(UV)尺度的定量协议。 在这个受控设置中,我们的M-PINN再现了分散式解决方案,同时保持计算紧凑和可微分,为具有现实顶点,未熄灭效应和不确定性感知变体的扩展铺平了道路。
A Minkowskian physics-informed neural network approach (M–PINN) is formulated to solve the Dyson–Schwinger integral equations (DSE) of quantum electrodynamics (QED) directly in Minkowski spacetime. Our novel strategy merges two complementary approaches: (i) a dispersive solver based on Lehmann representations and subtracted dispersion relations, and (ii) a M–PINN that learns the fermion mass function B(p^2), under the same truncation and renormalization configuration (quenched, rainbow, Landau g...