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Critical slowing down for the Krylov Dirac solver presents a major obstacle to further advances in lattice field theory as it approaches the continuum solution. We propose a new multi-grid approach for chiral fermions, applicable to both…

High Energy Physics - Lattice · Physics 2020-12-30 Richard C. Brower , M. A. Clark , Dean Howarth , Evan S. Weinberg

This paper presents a deep learning-based de-homogenization method for structural compliance minimization. By using a convolutional neural network to parameterize the mapping from a set of lamination parameters on a coarse mesh to a…

Machine Learning · Computer Science 2021-11-03 Martin O. Elingaard , Niels Aage , J. Andreas Bærentzen , Ole Sigmund

Within the domain wall-mediated electroweak baryogenesis, we study fermion scattering off a CP-violating wall in the background of an uniform magnetic field. In particular, we calculate the asymmetry between the reflection coefficients for…

Astrophysics · Physics 2009-11-11 Leonardo Campanelli , Paolo Cea , Gian Luigi Fogli , Luigi Tedesco

We determine the critical momenta for chiral fermions in the domain wall model recently suggested by Kaplan. For a wide range of domain wall masses $m$ and Wilson couplings $r$ we explicitly exhibit the regions in momentum space where the…

High Energy Physics - Lattice · Physics 2009-10-22 Karl Jansen , Martin Schmaltz

We analyze a learning-to-optimize (L2O) algorithm that exploits parameter space symmetry to enhance optimization efficiency. Prior work has shown that jointly learning symmetry transformations and local updates improves meta-optimizer…

Machine Learning · Computer Science 2025-04-23 Guy Zamir , Aryan Dokania , Bo Zhao , Rose Yu

Much recent research has been conducted in the area of Bayesian learning, particularly with regard to the optimization of hyper-parameters via Gaussian process regression. The methodologies rely chiefly on the method of maximizing the…

Machine Learning · Statistics 2014-05-13 James Brofos

We present results of numerical experiments for neural networks with stochastic gradient-based optimization with adaptive momentum. This widely applied optimization has proved convergence and practical efficiency, but for long-run training…

Machine Learning · Computer Science 2024-08-23 Igor V. Netay

The alternating direction method of multipliers (ADMM) has emerged as a powerful technique for large-scale structured optimization. Despite many recent results on the convergence properties of ADMM, a quantitative characterization of the…

Optimization and Control · Mathematics 2016-11-17 Euhanna Ghadimi , André Teixeira , Iman Shames , Mikael Johansson

Massless fermions on scalar domain walls are considered. Two walls are established, corresponding to 5-dimensional static spacetime asymptotically Anti de-Sitter, differentiated by the symmetry around the wall, and in each case massless…

High Energy Physics - Theory · Physics 2019-08-05 Rommel Guerrero , R. Omar Rodriguez , Francisco Carreras

We present a lattice calculation of the $K\to\pi$ and $K\to 0$ matrix elements of the $\Delta S=1$ effective weak Hamiltonian which can be used to determine $\epsilon^\prime/\epsilon$ and the $\Delta I=1/2$ rule for $K$ decays in the…

High Energy Physics - Lattice · Physics 2009-10-31 T. Blum

We describe a way to optimize the chiral behavior of Wilson-type lattice fermion actions by studying the low energy real eigenmodes of the Dirac operator. We find a candidate action, the clover action with fat links with a tuned clover…

High Energy Physics - Lattice · Physics 2007-05-23 Thomas DeGrand , Anna Hasenfratz , Tamás G. Kovács

Dirac fermions coupled to gauge fields can exhibit the chiral anomaly even on a finite spatial lattice. A careful description of this phenomenon yields new insights into the nature of spin-charge relations and on-site symmetries (symmetries…

High Energy Physics - Theory · Physics 2018-11-16 Djordje Radicevic

We study fermion localization in gauge theory space. We consider four dimensional product gauge groups in which light chiral fermions transform under different gauge factors of the product group. This construction provides a suppression of…

High Energy Physics - Phenomenology · Physics 2008-11-26 Witold Skiba , David Smith

It is well known that evolutionary algorithms (EAs) achieve peak performance only when their parameters are suitably tuned to the given problem. Even more, it is known that the best parameter values can change during the optimization…

Neural and Evolutionary Computing · Computer Science 2020-06-22 Arina Buzdalova , Carola Doerr , Anna Rodionova

Using lattice QCD simulations with $N_f = 2$ dynamical fermions, we study the axial $U(1)$ symmetry, topological charge, and Dirac eigenvalue spectra in the high-temperature phase in which the chiral symmetry is restored. Our gauge…

High Energy Physics - Lattice · Physics 2020-07-10 Kei Suzuki , Sinya Aoki , Yasumichi Aoki , Guido Cossu , Hidenori Fukaya , Shoji Hashimoto

Quantum Chromodynamics (QCD) is the fundamental theory for the interaction between quarks and gluons. It manifests as the short-range strong interaction inside the nucleus, and plays an important role in the evolution of the early universe,…

High Energy Physics - Lattice · Physics 2013-08-14 Ting-Wai Chiu

We consider Kaplan's domain wall fermions in the presence of an Anti-de Sitter (AdS) background in the extra dimension. Just as in the flat space case, in a completely vector-like gauge theory defined after discretizing this extra…

High Energy Physics - Lattice · Physics 2009-09-17 Tanmoy Bhattacharya , Csaba Csaki , Matthew R. Martin , Yuri Shirman , John Terning

We present results for the static interquark potential, light meson and baryon masses, and light pseudoscalar meson decay constants obtained from simulations of domain wall QCD with one dynamical flavour approximating the $s$ quark, and two…

Magnetic domain-walls (DWs) with a preferred chirality exhibit very efficient current-driven motion. Since structural inversion asymmetry (SIA) is required for their stability, the observation of chiral domain walls in highly symmetric…

Mesoscale and Nanoscale Physics · Physics 2014-04-14 Jeroen H. Franken , Mark Herps , Henk J. M. Swagten , Bert Koopmans

The performance of a machine learning model degrades when it is applied to data from a similar but different domain than the data it has initially been trained on. To mitigate this domain shift problem, domain adaptation (DA) techniques…

Machine Learning · Computer Science 2024-10-08 Felix Ott , David Rügamer , Lucas Heublein , Bernd Bischl , Christopher Mutschler
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