Physical Review E (Computational physics)

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Recently published articles in Phys. Rev. E in the Table of Content section "Computational physics"
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Chord length sampling with memory effects for spatially heterogeneous Markov media: Application to the rod model

Tue, 03/19/2024 - 10:00

Author(s): A. Tentori, C. Larmier, J. Durand, B. Cochet, and A. Zoia

In this work we propose a modified Chord Length Sampling (CLS) algorithm, endowed with two layers of “memory effects,” aimed at solving particle transport problems in one-dimensional spatially nonhomogeneous Markov media. CLS algorithms are a family of Monte Carlo methods which account for the stoch…


[Phys. Rev. E 109, 035302] Published Tue Mar 19, 2024

Color-gradient-based phase-field equation for multiphase flow

Fri, 03/08/2024 - 10:00

Author(s): Reza Haghani, Hamidreza Erfani, James E. McClure, Eirik Grude Flekkøy, and Carl Fredrik Berg

In this paper, the underlying problem with the color-gradient (CG) method in handling density-contrast fluids is explored. It is shown that the CG method is not fluid invariant. Based on nondimensionalizing the CG method, a phase-field interface-capturing model is proposed which tackles the difficul…


[Phys. Rev. E 109, 035301] Published Fri Mar 08, 2024

Wave amplitude gain within wedge waveguides through scattering by simple obstacles

Tue, 02/27/2024 - 10:00

Author(s): A. L. Azevedo, A. C. Maioli, F. Teston, M. R. Sales, F. M. Zanetti, and M. G. E. da Luz

Wave confinement, e.g., in waveguides, gives rise to a huge number of distinct phenomena. Among them, amplitude gain is a recurrent and relevant effect in undulatory processes. Using a general purpose protocol to solve wave equations, the boundary wall method, we demonstrate that for relatively simp…


[Phys. Rev. E 109, 025303] Published Tue Feb 27, 2024

Interpretable conservation laws as sparse invariants

Tue, 02/27/2024 - 10:00

Author(s): Ziming Liu, Patrick Obin Sturm, Saketh Bharadwaj, Sam J. Silva, and Max Tegmark

Discovering conservation laws for a given dynamical system is important but challenging. In a theorist setup (differential equations and basis functions are both known), we propose the sparse invariant detector (SID), an algorithm that autodiscovers conservation laws from differential equations. Its…


[Phys. Rev. E 109, L023301] Published Tue Feb 27, 2024

Phase-field lattice Boltzmann model with singular mobility for quasi-incompressible two-phase flows

Thu, 02/15/2024 - 10:00

Author(s): Jin Bao and Zhaoli Guo

In this paper, a lattice Boltzmann for quasi-incompressible two-phase flows is proposed based on the Cahn-Hilliard phase-field theory, which can be viewed as an improved model of a previous one [Yang and Guo, Phys. Rev. E 93, 043303 (2016)]. The model is composed of two LBE's, one for the Cahn-Hilli…


[Phys. Rev. E 109, 025302] Published Thu Feb 15, 2024

Macroscopic finite-difference scheme based on the mesoscopic regularized lattice-Boltzmann method

Mon, 02/05/2024 - 10:00

Author(s): Xi Liu, Ying Chen, Zhenhua Chai, and Baochang Shi

In this paper, we develop a macroscopic finite-difference scheme from the mesoscopic regularized lattice Boltzmann (RLB) method to solve the Navier-Stokes equations (NSEs) and convection-diffusion equation (CDE). Unlike the commonly used RLB method based on the evolution of a set of distribution fun…


[Phys. Rev. E 109, 025301] Published Mon Feb 05, 2024

Simulating ${\mathbb{Z}}_{2}$ lattice gauge theory on a quantum computer

Fri, 01/26/2024 - 10:00

Author(s): Clement Charles, Erik J. Gustafson, Elizabeth Hardt, Florian Herren, Norman Hogan, Henry Lamm, Sara Starecheski, Ruth S. Van de Water, and Michael L. Wagman

The utility of quantum computers for simulating lattice gauge theories is currently limited by the noisiness of the physical hardware. Various quantum error mitigation strategies exist to reduce the statistical and systematic uncertainties in quantum simulations via improved algorithms and analysis …


[Phys. Rev. E 109, 015307] Published Fri Jan 26, 2024

Energy, temperature, and heat capacity in discrete classical dynamics

Wed, 01/24/2024 - 10:00

Author(s): Søren Toxvaerd

Simulations of objects with classical dynamics are in fact a particular version of discrete dynamics, since almost all the classical dynamics simulations in natural science are performed with the use of the simple “leapfrog” or “Verlet” algorithm. It was, however, Newton who in Principia, Propositio…


[Phys. Rev. E 109, 015306] Published Wed Jan 24, 2024

Growth regimes in three-dimensional phase separation of liquid-vapor systems

Tue, 01/23/2024 - 10:00

Author(s): G. Negro, G. Gonnella, A. Lamura, S. Busuioc, and V. Sofonea

The liquid-vapor phase separation is investigated via lattice Boltzmann simulations in three dimensions. After expressing length and time scales in reduced physical units, we combined data from several large simulations (on 5123 nodes) with different values of viscosity, surface tension, and tempera…


[Phys. Rev. E 109, 015305] Published Tue Jan 23, 2024

Particles on demand method: Theoretical analysis, simplification techniques, and model extensions

Mon, 01/22/2024 - 10:00

Author(s): N. G. Kallikounis and I. V. Karlin

The particles on demand method [Phys. Rev. Lett. 121, 130602 (2018)] was recently formulated with a conservative finite-volume discretization and validated against challenging benchmarks. In this work, we focus on the properties of the reference frame transformation and its implications on the accur…


[Phys. Rev. E 109, 015304] Published Mon Jan 22, 2024

Noise-cancellation algorithm for simulations of Brownian particles

Thu, 01/18/2024 - 10:00

Author(s): Regina Rusch, Thomas Franosch, and Gerhard Jung

We investigate the usage of a recently introduced noise-cancellation algorithm for Brownian simulations to enhance the precision of measuring transport properties such as the mean-square displacement or the velocity-autocorrelation function. The algorithm is based on explicitly storing the pseudoran…


[Phys. Rev. E 109, 015303] Published Thu Jan 18, 2024

Physically interpretable approximations of many-body spectral functions

Thu, 01/11/2024 - 10:00

Author(s): Shubhang Goswami, Kipton Barros, and Matthew R. Carbone

The rational function approximation provides a natural and interpretable representation of response functions such as the many-body spectral functions. We apply the vector fitting (VFIT) algorithm to fit a variety of spectral functions calculated from the Holstein model of electron-phonon interactio…


[Phys. Rev. E 109, 015302] Published Thu Jan 11, 2024

Self-consistent force scheme in the spectral multiple-relaxation-time lattice Boltzmann model

Wed, 01/10/2024 - 10:00

Author(s): Xuhui Li (李旭晖), Zuoxu Li (李作旭), Wenyang Duan (段文洋), and Xiaowen Shan (单肖文)

In the present work, the force term is first derived in the spectral multiple-relaxation-time high-order lattice Boltzmann model. The force term in the Boltzmann equation is expanded in the Hermite temperature rescaled central moment space (RCM), instead of the Hermite raw moment space (RM). The con…


[Phys. Rev. E 109, 015301] Published Wed Jan 10, 2024

Resampling schemes in population annealing: Numerical and theoretical results

Tue, 12/26/2023 - 10:00

Author(s): Denis Gessert, Wolfhard Janke, and Martin Weigel

The population annealing algorithm is a population-based equilibrium version of simulated annealing. It can sample thermodynamic systems with rough free-energy landscapes more efficiently than standard Markov chain Monte Carlo alone. A number of parameters can be fine-tuned to improve the performanc…


[Phys. Rev. E 108, 065309] Published Tue Dec 26, 2023

Explicitly correlated Gaussians for high-precision variational calculations of ${S}^{e}, {P}^{e}$, and ${D}^{e}$ states of quantum systems: An efficient algorithm

Fri, 12/22/2023 - 10:00

Author(s): Toreniyaz Shomenov and Sergiy Bubin

In this work we consider an efficient algorithm for variational calculations of quantum few-particle systems in S, P, and D states of the even parity using all-particle explicitly correlated Gaussian (ECG) basis sets. We primarily focus on the description of states where the dominant configuration c…


[Phys. Rev. E 108, 065308] Published Fri Dec 22, 2023

Calculating the classical virial expansion using automated algebra

Thu, 12/21/2023 - 10:00

Author(s): Aaron M. Miller and Joaquín E. Drut

Using schematic model potentials, we calculate exactly the virial coefficients of a classical gas up to sixth order and use them to calculate the virial expansion of basic thermodynamic quantities such as pressure, density, and compressibility. At sufficiently strong couplings, as expected, the viri…


[Phys. Rev. E 108, 065307] Published Thu Dec 21, 2023

Generalized equilibria for color-gradient lattice Boltzmann model based on higher-order Hermite polynomials: A simplified implementation with central moments

Tue, 12/19/2023 - 10:00

Author(s): Shimpei Saito (齋藤慎平), Naoki Takada (高田尚樹), Soumei Baba (馬場宗明), Satoshi Someya (染矢聡), and Hiroshi Ito (伊藤博)

We propose generalized equilibria of a three-dimensional color-gradient lattice Boltzmann model for two-component two-phase flows using higher-order Hermite polynomials. Although the resulting equilibrium distribution function, which includes a sixth-order term on the velocity, is computationally cu…


[Phys. Rev. E 108, 065305] Published Tue Dec 19, 2023

Monte Carlo generation of localized particle trajectories

Tue, 12/19/2023 - 10:00

Author(s): Ivan Ahumada and James P. Edwards

Monte Carlo simulations of path integrals suffer from reduced precision at large times due to undersampling. To address this problem the authors propose a scheme where the sampling trajectories are concentrated in more important regions, and they show the effectiveness of their method with some simple test cases.


[Phys. Rev. E 108, 065306] Published Tue Dec 19, 2023

Machine learning for structure-property mapping of Ising models: Scalability and limitations

Wed, 12/13/2023 - 10:00

Author(s): Zhongzheng Tian, Sheng Zhang, and Gia-Wei Chern

We present a scalable machine learning (ML) framework for predicting intensive properties and particularly classifying phases of Ising models. Scalability and transferability are central to the unprecedented computational efficiency of ML methods. In general, linear-scaling computation can be achiev…


[Phys. Rev. E 108, 065304] Published Wed Dec 13, 2023

Sampling diverse near-optimal solutions via algorithmic quantum annealing

Mon, 12/11/2023 - 10:00

Author(s): Masoud Mohseni, Marek M. Rams, Sergei V. Isakov, Daniel Eppens, Susanne Pielawa, Johan Strumpfer, Sergio Boixo, and Hartmut Neven

Sampling a diverse set of high-quality solutions for hard optimization problems is of great practical relevance in many scientific disciplines and applications, such as artificial intelligence and operations research. One of the main open problems is the lack of ergodicity, or mode collapse, for typ…


[Phys. Rev. E 108, 065303] Published Mon Dec 11, 2023

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