TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysCore.8.3.054
TI  - Physics-informed neural networks viewpoint for solving the Dyson-Schwinger equations of quantum electrodynamics
PY  - 2025/08/20
UR  - https://www.scipost.org/SciPostPhysCore.8.3.054
JF  - SciPost Physics Core
JA  - SciPost Phys. Core
VL  - 8
IS  - 3
SP  - 054
A1  - Carmo Terin, Rodrigo
AB  - Physics-informed neural networks (PINNs) are employed to solve the Dyson-Schwinger equations of quantum electrodynamics (QED) in Euclidean space, with a focus on the non-perturbative generation of the fermion's dynamical mass function in the Landau gauge. By inserting the integral equation directly into the loss function, our PINN framework enables a single neural network to learn a continuous and differentiable representation of the mass function over a spectrum of momenta. Also, we benchmark our approach against a traditional numerical algorithm showing the main differences among them. Our novel strategy, which is expected to be extended to other quantum field theories, is the first step towards forefront applications of machine learning in high-level theoretical physics.
ER  -

@Article{10.21468/SciPostPhysCore.8.3.054,
	title={{Physics-informed neural networks viewpoint for solving the Dyson-Schwinger equations of quantum electrodynamics}},
	author={Rodrigo Carmo Terin},
	journal={SciPost Phys. Core},
	volume={8},
	pages={054},
	year={2025},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCore.8.3.054},
	url={https://scipost.org/10.21468/SciPostPhysCore.8.3.054},
}