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Quantum field theories underlie all of our understanding of the fundamental forces of nature. The are relatively few first principles approaches to the study of quantum field theories [such as quantum chromodynamics (QCD) relevant to the…

High Energy Physics - Lattice · Physics 2010-03-04 F. D. R. Bonnet , Derek B. Leinweber , Anthony G. Williams

Simulation of Lattice QCD is a challenging computational problem. Currently, technological trends in computation show multiple divergent models of computation. We are witnessing homogeneous multi-core architectures, the use of accelerator…

High Energy Physics - Lattice · Physics 2008-08-13 K. Ibrahim , J. Jaeger , Z. Liu , L. N. Pouchet , P. Lesnicki , L. Djoudi , D. Barthou , F. Bodin , C. Eisenbeis , G. Grosdidier , O. Pene , P. Roudeau

The Discrete Dislocation (DD) analysis and its computional modeling have been advanced significantly over the past decade. This progress has been further magnified by the idea to couple DD with continuum mechanics analysis in association…

Materials Science · Physics 2007-05-23 H. M. Zbib , M. Hiratani , M. Shehade

The present research is developed into the realm of industrial design engineering and additive manufacturing by introducing a parametric design model and adaptive mechanical analysis for a new lattice structure, with a focus on 3D additive…

Lattice structures have been widely used in various applications of additive manufacturing due to its superior physical properties. If modeled by triangular meshes, a lattice structure with huge number of struts would consume massive…

Computational Geometry · Computer Science 2021-02-02 Shengjun Liu , Tao Liu , Qiang Zou , Weiming Wang , Eugeni L. Doubrovski , Charlie C. L. Wang

As point clouds are 3D signals with permutation invariance, most existing works train their reconstruction networks by measuring shape differences with the average point-to-point distance between point clouds matched with predefined rules.…

Computer Vision and Pattern Recognition · Computer Science 2023-12-29 Tianxin Huang , Qingyao Liu , Xiangrui Zhao , Jun Chen , Yong Liu

Recently, numerous end-to-end optimized image compression neural networks have been developed and proved themselves as leaders in rate-distortion performance. The main strength of these learnt compression methods is in powerful nonlinear…

Image and Video Processing · Electrical Eng. & Systems 2023-04-26 Xi Zhang , Xiaolin Wu

Quasi-Monte Carlo (QMC) integration of output functionals of solutions of the diffusion problem with a log-normal random coefficient is considered. The random coefficient is assumed to be given by an exponential of a Gaussian random field…

Numerical Analysis · Mathematics 2017-01-24 Yoshihito Kazashi

We construct numerical basis function sets on a lattice, whose spatial extension is scalable from single lattice sites to the continuum limit. They allow us to compute small and large bound states with comparable, moderate effort. Adopting…

Other Condensed Matter · Physics 2011-08-17 A. Alvermann , P. B. Littlewood , H. Fehske

Failure in brittle materials under dynamic loading conditions is a result of the propagation and coalescence of microcracks. Simulating this mechanism at the continuum level is computationally expensive or, in some cases, intractable. The…

Energy dissipation by fast crystalline defects takes place mainly through the resonant interaction of their cores with periodic lattice. We show that the resultant effective friction can be reduced to zero by appropriately tuned acoustic…

Mesoscale and Nanoscale Physics · Physics 2020-11-11 N. Gorbushin , G. Mishuris , L. Truskinovsky

In this paper a lattice model for diffusional transport of particles in the interphase cell nucleus is proposed. Dense networks of chromatin fibers are created by three different methods: randomly distributed, non-interconnected obstacles,…

Biological Physics · Physics 2009-11-13 Annika Wedemeier , Holger Merlitz , Chen-Xu Wu , Jörg Langowski

We propose a machine learning approach to address a key challenge in materials science: predicting how fractures propagate in brittle materials under stress, and how these materials ultimately fail. Our methods use deep learning and train…

False vacuum decay in scalar quantum field theory (QFT) is a cornerstone of early Universe cosmology and high energy physics, yet its real-time dynamics is essentially inaccessible to classical computation due to its non-perturbative,…

Quantum Physics · Physics 2025-11-27 Steven Abel , Michael Spannowsky , Simon Williams

In this paper we introduce learnable lattice vector quantization and demonstrate its effectiveness for learning discrete representations. Our method, termed LL-VQ-VAE, replaces the vector quantization layer in VQ-VAE with lattice-based…

Machine Learning · Computer Science 2023-10-17 Ahmed Khalil , Robert Piechocki , Raul Santos-Rodriguez

The derivative discontinuity of the exchange-correlation (xc) energy at integer particle number is a property of the exact, unknown xc functional of density functional theory (DFT) which is absent in many popular local and semilocal…

Strongly Correlated Electrons · Physics 2015-06-11 Gao Xianlong , A-Hai Chen , I. V. Tokatly , S. Kurth

We introduce a lattice model for a static and isotropic system of relativistic fermions. An action principle is formulated, which describes a particle-particle interaction of all fermions. The model is designed specifically for a numerical…

Mathematical Physics · Physics 2014-11-18 Felix Finster , Wätzold Plaum

This paper introduces a new discrete fracture model accounting for non-isothermal compositional multiphase Darcy flows and complex networks of fractures with intersecting, immersed and non immersed fractures. The so called…

Numerical Analysis · Mathematics 2017-07-13 Feng Xing , Roland Masson , Simon Lopez

The problem of designing physical-layer network coding (PNC) schemes via nested lattices is considered. Building on the compute-and-forward (C&F) relaying strategy of Nazer and Gastpar, who demonstrated its asymptotic gain using…

Information Theory · Computer Science 2015-03-19 Chen Feng , Danilo Silva , Frank R. Kschischang

Exascale computing could soon enable a predictive theory of nuclear structure and reactions rooted in the Standard Model, with quantifiable and systematically improvable uncertainties. Such a predictive theory will help exploit experiments…