Related papers: Gradient flow scale setting with tree-level improv…
We use the Yang-Mills gradient flow to study the mixing of CP-violating pure gauge operators in continuum QCD with special attention to Weinberg's d=6 purely gluonic operator. The gradient flow allows for a relatively clear derivation of…
In this paper, we study higher-order-accurate-in-time minimizing movements schemes for Wasserstein gradient flows. We introduce a novel accelerated second-order scheme, leveraging the differential structure of the Wasserstein space in both…
First results from simulations of improved actions for both gauge fields and staggered fermion fields in three dimensional QCD are presented. This work provides insight into some issues of relevance to lattice theories in four dimensions.…
We test here our recently introduced new lattice method for the $\beta$-function defined over infinite Euclidean space-time in the continuum from scale changes generated by infinitesimal or finite steps of the renormalized gauge coupling on…
We present first results of the scale setting for QCD with Nf=3+1 dynamical quarks on the lattice. We use a recently proposed massive renormalization scheme with a non-perturbatively determined clover coefficient. To relate the bare…
By using Symanzik's improvement program, we study on-shell improved lattice QCD with staggered fermions. We find that there are as many as 15 independent lattice operators of dimension of six~(including both gauge and fermion operators)…
We review our recent study on the QCD static force using gradient flow at next-to-leading order in the strong coupling. The QCD static force has the advantage of being free of the $O(\Lambda_{\text{QCD}})$ renormalon appearing in the static…
Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different action parameters. This work demonstrates how these…
We propose a new method to determine the quark mass by using bilinear operators of the flowed quark field defined within the gradient-flow formalism. This method enables the quark mass determination through a comparison of perturbative…
The autoencoder model uses an encoder to map data samples to a lower dimensional latent space and then a decoder to map the latent space representations back to the data space. Implicitly, it relies on the encoder to approximate the inverse…
We present results of a lattice QCD application of a coordinate space renormalization scheme for the extraction of renormalization constants for flavour non-singlet bilinear quark operators. The method consists in the analysis of the…
We study the gradient flow of the Allen-Cahn equation with fixed boundary contact angle in Euclidean domains for initial data with bounded energy. Under general assumptions, we establish both interior and boundary convergence properties for…
Variational inference is a technique that approximates a target distribution by optimizing within the parameter space of variational families. On the other hand, Wasserstein gradient flows describe optimization within the space of…
We compare lattice QCD determinations of topological susceptibility using a gluonic definition from the gradient flow and a fermionic definition from the spectral projector method. We use ensembles with dynamical light, strange and charm…
The Schr\"odinger Functional (quantum/lattice field theory with Dirichlet boundary conditions) is a powerful tool in the non-perturbative improvement and for the study of other aspects of lattice QCD. Here we adapt it to improved gluon and…
In part I: We find a series physical scales such as 1) Planck scale, 2) Minimal approximate grand unification SU(5), 3) the mass scale of the see saw model right handed or Majorana neutrinoes, some invented scale with many scalar bosons,…
Recently the Yang-Mills gradient flow of pure SU(3) lattice gauge theory has been calculated in the range from $\beta=6/g_0^2=6.3$ to~7.5 (Asakawa et al.), where $g_0^2$ is the bare coupling constant of the SU(3) Wilson action. Estimates of…
Gromov-Wasserstein (GW) is a powerful tool to compare probability measures whose supports are in different metric spaces. GW suffers however from a computational drawback since it requires to solve a complex non-convex quadratic program. We…
We derive the system of differential equations for the gradient flow characterizing the training process of linear in-context learning in full generality. Next, we explore the geometric structure of the gradient flows in two instances,…
Recurrent neural networks have shown remarkable success in modeling sequences. However low resource situations still adversely affect the generalizability of these models. We introduce a new family of models, called Lattice Recurrent Units…