arXiv:2607. 21548v1 Announce Type: cross Abstract: The coupled ghost and gluon Dyson--Schwinger equations (DSEs) of four-dimensional Landau-gauge Yang--Mills (YM) theory are solved with a neural representation trained only from renormalized equation residuals.
By Rodrigo Carmo Terin
arXiv:2510. 24728v2 Announce Type: replace-cross Abstract: We study neural reconstructions of quenched rainbow quantum electrodynamics (QED) Dyson--Schwinger benchmarks in Minkowski-related kinematics.
By Rodrigo Carmo Terin
A new preprint shows GPT-5. 2 proposing a new formula for a gluon amplitude, later formally proved and verified by OpenAI and academic collaborators.
arXiv:2605. 23391v2 Announce Type: replace Abstract: Physics-informed neural networks (PINNs) for coupled multiphysics systems suffer systematic accuracy degradation as inter-equation coupling strengthens.
By Youngjae Park, Jaemin Kim, Junghwa Hong
arXiv:2603. 02984v2 Announce Type: replace-cross Abstract: Normalizing flows can be used to construct unbiased, reduced-variance estimators for lattice field theory observables that are defined by a derivative with respect to action parameters.
By Ryan Abbott, Denis Boyda, Yang Fu, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-L\'opez, Phiala E. Shanahan, Julian M. Urban
arXiv:2607. 13609v1 Announce Type: new Abstract: Recovering a latent potential from observed flow on a directed graph (a discrete Poisson problem with Dirichlet boundaries) is ill-posed, and the standard fix backfires: ridge regularization shrinks toward a gauge-meaningless origin, collapsing and reversing the recovered ordering ($+0.
By Mohammad Forouhesh