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A new Monte Carlo method is proposed for fermion systems interacting with classical degrees of freedom. To obtain a weight for each Monte Carlo sample with a fixed configuration of classical variables, the moment expansion of the density of…

强关联电子 · 物理学 2015-06-24 Yukitoshi Motome , Nobuo Furukawa

Microscopic models of classical degrees of freedom coupled to non-interacting fermions occur in many different contexts. Prominent examples from solid state physics are descriptions of colossal magnetoresistance manganites and diluted…

统计力学 · 物理学 2009-06-16 Alexander Weiße

Large classes of materials systems in physics and engineering are governed by magnetic and electrostatic interactions. Continuum or mesoscale descriptions of such systems can be cast in terms of integral equations, whose direct…

We suggest and implement a new Monte Carlo strategy for correlated models involving fermions strongly coupled to classical degrees of freedom, with accurate handling of quenched disorder as well. Current methods iteratively diagonalise the…

强关联电子 · 物理学 2007-05-23 Sanjeev Kumar , Pinaki Majumdar

Diffusion Monte Carlo (DMC) simulations for fermions are becoming the standard to provide high quality reference data in systems that are too large to be investigated via quantum chemical approaches. DMC with the fixed-node approximation…

化学物理 · 物理学 2016-07-06 Andrea Zen , Sandro Sorella , Michael J. Gillan , Angelos Michaelides , Dario Alfè

An algorithm for separating the high- and low-frequency molecular dynamics modes in Hybrid Monte Carlo simulations of gauge theories with dynamical fermions is presented. The separation is based on splitting the pseudo-fermion action into…

高能物理 - 格点 · 物理学 2008-11-26 A. Ali Khan , T. Bakeyev , M. Göckeler , R. Horsley , D. Pleiter , P. Rakow , A. Schäfer , G. Schierholz , H. Stüben

This paper describes a state-of-the-art parallel Lattice QCD Monte Carlo code for staggered fermions, purposely designed to be portable across different computer architectures, including GPUs and commodity CPUs. Portability is achieved…

The Green's function Monte Carlo (GFMC) method provides accurate solutions to the nuclear many-body problem and predicts properties of light nuclei starting from realistic two- and three-body interactions. Controlling the GFMC fermion-sign…

核理论 · 物理学 2023-04-07 Gurtej Kanwar , Alessandro Lovato , Noemi Rocco , Michael Wagman

We develop a GPU-accelerated hybrid quantum Monte Carlo (QMC) algorithm to solve the fundamental yet difficult problem of $U(1)$ gauge field coupled to fermions, which gives rise to a $U(1)$ Dirac spin liquid state under the description of…

强关联电子 · 物理学 2026-02-27 Kexin Feng , Chuang Chen , Zi Yang Meng

We present a hybrid quantum-classical Green's function Monte Carlo (GFMC) algorithm for estimating the excited states of the nuclear shell model. The conventional GFMC method, widely used to find the ground state of a quantum many-body…

量子物理 · 物理学 2025-08-25 Yongdan Yang , Ruyu Yang , Xiaosi Xu

This paper presents the first parallel implementation of the novel "Interpolated Factored Green Function" (IFGF) method introduced recently for the accelerated evaluation of discrete integral operators arising in wave scattering and other…

数值分析 · 数学 2022-05-12 Christoph Bauinger , Oscar P. Bruno

A software library is presented for the polynomial expansion method (PEM) of the density of states (DOS) developed by two of the authors (N.F. and Y. M.). The library provides all necessary functions for the use of the PEM and its truncated…

强关联电子 · 物理学 2007-05-23 G. Alvarez , C. Sen , N. Furukawa , Y. Motome , E. Dagotto

We address the calculation of dynamical correlation functions for many fermion systems at zero temperature, using the auxiliary-field quantum Monte Carlo method. The two-dimensional Hubbard hamiltonian is used as a model system. Although…

强关联电子 · 物理学 2016-08-24 Ettore Vitali , Hao Shi , Mingpu Qin , Shiwei Zhang

We present a quantum Monte-Carlo algorithm for computing the perturbative expansion in power of the coupling constant $U$ of the out-of-equilibrium Green's functions of interacting Hamiltonians of fermions. The algorithm extends the one…

强关联电子 · 物理学 2019-09-23 Corentin Bertrand , Olivier Parcollet , Antoine Maillard , Xavier Waintal

Variational Monte Carlo is a many-body numerical method that scales well with system size. It has been extended to study the Green function only recently by Charlebois and Imada (2020). Here we generalize the approach to systems with open…

强关联电子 · 物理学 2022-12-20 P. Rosenberg , D. Sénéchal , A. -M. S. Tremblay , M. Charlebois

The Multilevel Monte Carlo (MLMC) method has proven to be an effective variance-reduction statistical method for Uncertainty Quantification (UQ) in Partial Differential Equation (PDE) models, combining model computations at different levels…

数学软件 · 计算机科学 2023-05-24 Santiago Badia , Jerrad Hampton , Javier Principe

Kernel matrix-vector product is ubiquitous in many science and engineering applications. However, a naive method requires $O(N^2)$ operations, which becomes prohibitive for large-scale problems. We introduce a parallel method that provably…

数学软件 · 计算机科学 2021-04-30 Ruoxi Wang , Chao Chen , Jonghyun Lee , Eric Darve

Numerically exact continuous-time Quantum Monte Carlo algorithm for finite fermionic systems with non-local interactions is proposed. The scheme is particularly applicable for general multi-band time-dependent correlations since it does not…

强关联电子 · 物理学 2009-11-10 A. N. Rubtsov , A. I. Lichtenstein

The Hybrid Monte Carlo algorithm is adapted to the simulation of a system of classical degrees of freedom coupled to non self-interacting lattices fermions. The diagonalization of the Hamiltonian matrix is avoided by introducing a…

强关联电子 · 物理学 2009-10-31 J. L. Alonso , L. A. Fernandez , F. Guinea , V. Laliena , V. Martin-Mayor

In recent years, the Hamiltonian Monte Carlo (HMC) algorithm has been found to work more efficiently compared to other popular Markov Chain Monte Carlo (MCMC) methods (such as random walk Metropolis-Hastings) in generating samples from a…

统计计算 · 统计学 2014-02-18 Andrew L. Beam , Sujit K. Ghosh , Jon Doyle
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